U.S. patent number 9,323,888 [Application Number 13/600,043] was granted by the patent office on 2016-04-26 for detecting and classifying copy number variation.
This patent grant is currently assigned to Verinata Health, Inc.. The grantee listed for this patent is Richard P. Rava, Anupama Srinivasan. Invention is credited to Richard P. Rava, Anupama Srinivasan.
United States Patent |
9,323,888 |
Rava , et al. |
April 26, 2016 |
Detecting and classifying copy number variation
Abstract
The invention provides a method for determining copy number
variations (CNV) of a sequence of interest in a test sample that
comprises a mixture of nucleic acids that are known or are
suspected to differ in the amount of one or more sequence of
interest. The method comprises a statistical approach that accounts
for accrued variability stemming from process-related,
interchromosomal and inter-sequencing variability. The method is
applicable to determining CNV of any fetal aneuploidy, and CNVs
known or suspected to be associated with a variety of medical
conditions. CNV that can be determined according to the method
include trisomies and monosomies of any one or more of chromosomes
1-22, X and Y, other chromosomal polysomies, and deletions and/or
duplications of segments of any one or more of the chromosomes,
which can be detected by sequencing only once the nucleic acids of
a test sample.
Inventors: |
Rava; Richard P. (Redwood City,
CA), Srinivasan; Anupama (Redwood City, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Rava; Richard P.
Srinivasan; Anupama |
Redwood City
Redwood City |
CA
CA |
US
US |
|
|
Assignee: |
Verinata Health, Inc. (San
Diego, CA)
|
Family
ID: |
48086371 |
Appl.
No.: |
13/600,043 |
Filed: |
August 30, 2012 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20130096011 A1 |
Apr 18, 2013 |
|
US 20160070853 A9 |
Mar 10, 2016 |
|
Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
Issue Date |
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13555037 |
Jul 20, 2012 |
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13191366 |
Jul 26, 2011 |
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12958352 |
Dec 1, 2010 |
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13600043 |
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13400028 |
Feb 17, 2012 |
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13009708 |
Jan 19, 2011 |
8700341 |
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13600043 |
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13445778 |
Apr 12, 2012 |
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13600043 |
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12958347 |
Dec 1, 2010 |
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13600043 |
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12958356 |
Dec 1, 2010 |
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13600043 |
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13482964 |
May 29, 2012 |
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12958353 |
Dec 1, 2010 |
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13600043 |
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PCT/US2012/031625 |
Mar 30, 2012 |
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13600043 |
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13087842 |
Apr 15, 2011 |
8532936 |
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61296464 |
Jan 19, 2010 |
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61474362 |
Apr 12, 2011 |
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61296358 |
Jan 19, 2010 |
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61407017 |
Oct 26, 2010 |
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61360837 |
Jul 1, 2010 |
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61455849 |
Oct 26, 2010 |
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61469236 |
Mar 30, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q
1/6809 (20130101); G16B 40/00 (20190201); G16B
30/00 (20190201); C12Q 1/6869 (20130101); C12Q
1/6809 (20130101); C12Q 2537/16 (20130101); C12Q
2537/165 (20130101); C12Q 2545/101 (20130101); C12Q
1/6883 (20130101); C12Q 1/6886 (20130101); C12Q
2537/16 (20130101); C12Q 2600/106 (20130101) |
Current International
Class: |
G06F
19/22 (20110101); G11C 17/00 (20060101); G06F
15/00 (20060101); C12Q 1/68 (20060101); G06F
19/24 (20110101) |
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WO |
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|
Primary Examiner: Brusca; John S
Attorney, Agent or Firm: Weaver Austin Villeneuve &
Sampson LLP
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of U.S. application Ser.
No. 13/555,037, filed Jul. 20, 2012, which is a
continuation-in-part of U.S. application Ser. No. 13/191,366, filed
on Jul. 26, 2011, which is a continuation-in-part of U.S.
application Ser. No. 12/958,352, filed on Dec. 1, 2010, which
claims priority to U.S. Provisional Application Nos. 61/296,358
filed Jan. 19, 2010 and 61/360,837 filed Jul. 1, 2010 and
61/407,017 and 61/455,849 both filed Oct. 26, 2010, all of which
are incorporated by reference in their entireties. This application
is also a continuation-in-part of U.S. application Ser. No.
13/400,028, filed on Feb. 17, 2012. This application is also a
continuation-in-part of U.S. application Ser. No. 13/009,708 filed
Jan. 19, 2011, which claims benefit of U.S. Provisional Patent
Application No. 61/296,464 filed Jan. 19, 2010, both of which are
incorporated herein by reference their entireties. This application
is also a continuation-in-part of U.S. application Ser. No.
13/445,778 filed Apr. 12, 2012, which claims benefit of U.S.
Provisional Patent Application No. 61/474,362 filed Apr. 12, 2011,
both of which are incorporated herein by reference in their
entireties. This application is also a continuation-in-part of U.S.
application Ser. No. 12/958,347 filed Dec. 1, 2010, which claims
benefit of U.S. Provisional Patent Application Nos. 61/296,358
filed Jan. 19, 2010 and 61/360,837 filed Jul. 1, 2010 and
61/407,017 and 61/455,849 both filed Oct. 26, 2010, all of which
are incorporated by reference in their entireties and for all
purposes. This application is also a continuation-in-part of U.S.
application Ser. No. 12/958,356 filed Dec. 1, 2010, which claims
benefit of U.S. Provisional Patent Application Nos. 61/296,358
filed Jan. 19, 2010 and 61/360,837 filed Jul. 1, 2010 and
61/407,017 and 61/455,849 both filed Oct. 26, 2010, all of which
are incorporated by reference in their entireties and for all
purposes. This application is also a continuation-in-part of U.S.
application Ser. No. 13/482,964, filed on May 29, 2012, which is a
continuation-in-part of U.S. application Ser. No. 12/958,353, filed
on Dec. 1, 2010. This application is also a continuation-in-part of
International Application PCT/US2012/031625, filed on Mar. 30,
2012, which claims benefit of U.S. Provisional Patent Application
No. 61/469,236, filed on Mar. 30, 2011. This application is also a
continuation-in-part of U.S. application Ser. No. 13/087,842, filed
Apr. 15, 2011, by Richard P. Rava, entitled "Normalizing
Chromosomes for the Determination and Verification of Common and
Rare Chromosomal Aneuploidies."
Claims
What is claimed is:
1. A method for classifying a copy number variation in a fetal
genome, the method comprising: (a) receiving sequence reads from
fetal and maternal nucleic acids in a maternal test sample obtained
from a mother carrying a fetus, wherein the sequence reads are
provided in an electronic format; (b) aligning the sequence reads
to one or more bins from a reference sequence using a computing
apparatus and thereby providing sequence tags corresponding to the
sequence reads, wherein each bin contains a subsequence within the
reference sequence; (c) computationally identifying a number of
those sequence tags that are from one or more bins by using the
computing apparatus and determining that a first bin of interest in
the fetal genome harbors a copy number variation; (d) calculating a
first fetal fraction value by a first method that does not use
information from sequence tags from the first bin of interest; (e)
calculating a second fetal fraction value by a second method that
uses information from the sequence tags from the first bin of
interest, wherein the second method includes a ploidy assumption
that the fetal genome has a complete aneuploidy within the
subsequence of the first bin of interest; and (f) comparing the
first fetal fraction value and the second fetal fraction value and
using the comparison to classify the copy number variation of the
first bin of interest in the fetal genome as harboring a complete
aneuploidy or another type of copy number variation.
2. The method of claim 1, wherein the comparing the first fetal
fraction value and the second fetal fraction value comprises
determining whether the two fetal fraction values are approximately
equal.
3. The method of claim 2, further comprising determining that the
two fetal fraction values are approximately equal, and thereby
determining that the ploidy assumption of the second method is
true.
4. The method of claim 2, wherein comparing the first fetal
fraction value and the second fetal fraction value indicates that
the two fetal fraction values are not approximately equal, and
further comprising analyzing the tag information for the first bin
of interest to determine whether (i) the first bin of interest
harbors a partial aneuploidy, or (ii) the fetus is a mosaic.
5. The method of claim 4, wherein analyzing the tag information for
the first bin of interest comprises: dividing the sequence for the
first bin of interest into a plurality of sub-bins; determining
whether any of said sub-bins contains significantly more or
significantly less nucleic acid than one or more other sub-bins;
and if any of said plurality of sub-bins contain significantly more
or significantly less nucleic acid than one or more other sub-bins,
determining that the first bin of interest harbors a partial
aneuploidy.
6. The method of claim 5, further comprising determining that a
sub-bin of the first bin of interest containing significantly more
or significantly less nucleic acid than one or more other sub-bins
harbors the partial aneuploidy.
7. The method of claim 4, wherein analyzing the tag information for
the first bin of interest comprises: dividing the sequence for the
first bin of interest into a plurality of sub-bins; determining
whether any of said sub-bins contains significantly more or
significantly less nucleic acid than one or more other sub-bins;
and if none of said sub-bins contain significantly more or
significantly less nucleic acid than one or more other sub-bins,
determining that the fetus is a mosaic.
8. The method of claim 1, wherein if the first fetal fraction value
is not approximately equal to the second fetal fraction value, (i)
determining whether the copy number variation results from a
partial aneuploidy or a mosaic; and (ii) if the copy number
variation results from a partial aneuploidy, determining a locus of
the partial aneuploidy on the first bin of interest.
9. The method of claim 8, wherein determining the locus of the
partial aneuploidy on the first bin of interest comprises
categorizing the sequence tags for the first bin of interest into
sub-bins of blocks of nucleic acids in the first bin of interest;
and counting sequence tags in each sub-bin.
10. The method of claim 1, further comprising sequencing cell free
DNA from the maternal test sample to provide the sequence
reads.
11. The method of claim 10, further comprising obtaining the
maternal test sample from a pregnant organism.
12. The method of claim 1, wherein the aligning in (b) comprises
using the computing apparatus to align at least about 1 million
reads.
13. A computer program product comprising a non-transitory computer
readable medium on which is provided program instructions for
classifying a copy number variation in a fetal genome, the program
instructions comprising: (a) code for receiving sequence reads from
fetal and maternal nucleic acids in a maternal test sample obtained
from a mother carrying a fetus, wherein the sequence reads are
provided in an electronic format; (b) code for aligning the
sequence reads to one or more bins from a reference sequence using
a computing apparatus and thereby providing sequence tags
corresponding to the sequence reads, wherein each bin contains a
subsequence within the reference sequence; (c) code for
computationally identifying a number of those sequence tags that
are from one or more bins by using the computing apparatus and
determining that a first bin of interest in the fetal genome
harbors a copy number variation; (d) code for calculating a first
fetal fraction value by a first method that does not use
information from sequence tags from the first bin of interest; (e)
code for calculating a second fetal fraction value by a second
method that uses information from the sequence tags from the first
bin of interest, wherein the second method includes a ploidy
assumption that the fetal genome has a complete aneuploidy within
the subsequence of the first bin of interest; and (f) code for
comparing the first fetal fraction value and the second fetal
fraction value and using the comparison to classify the copy number
variation of the first bin of interest in the fetal genome as
harboring a complete aneuploidy or another type of copy number
variation.
14. A system for classifying a copy number variation in a fetal
genome, the system comprising: (a) an interface for receiving at
least about 10,000 sequence reads from fetal and maternal nucleic
acids in a maternal test sample obtained from a mother carrying a
fetus, wherein the sequence reads are provided in an electronic
format; (b) memory for storing, at least temporarily, a plurality
of said sequence reads; (c) a processor designed or configured with
p(i) aligning the sequence reads to one or more reference sequences
and thereby providing sequence tags corresponding to the sequence
reads; (ii) identifying a number of those sequence tags that are
from one or more bins of interest and determining that a first bin
of interest in the fetal genome harbors a copy number variation,
wherein each bin contains a subsequence within the reference
sequence; (iii) calculating a first fetal fraction value by a first
method that does not use information from sequence tags from the
first bin of interest; (iv) calculating a second fetal fraction
value by a second method that uses information from the sequence
tags from the first bin of interest, wherein the second method
includes a ploidy assumption that the fetal genome has a complete
aneuploidy within the subsequence of the first bin of interest; and
(v) comparing the first fetal fraction value and the second fetal
fraction value and using the comparison to classify the copy number
variation of the first bin of interest in the fetal genome as
harboring a complete aneuploidy or another type of copy number
variation.
15. The computer program product of claim 13, wherein the comparing
the first fetal fraction value and the second fetal fraction value
comprises determining whether the two fetal fraction values are
approximately equal.
16. The computer program product of claim 15, the program
instructions further comprising instructions for determining that
the two fetal fraction values are approximately equal, and thereby
determining that the ploidy assumption of the second method is
true.
17. The computer program product of claim 15, the program
instructions further comprising instructions for determining that
the two fetal fraction values are not approximately equal, and
analyzing the tag information for the first bin of interest to
determine whether (i) the first bin of interest harbors a partial
aneuploidy, or (ii) the fetus is a mosaic.
18. The computer program product of claim 17, wherein analyzing the
tag information for the first bin of interest comprises: dividing
the sequence for the first bin of interest into a plurality of
sub-bins; determining whether any of said sub-bins contains
significantly more or significantly less nucleic acid than one or
more other sub-bins; and if any of said plurality of sub-bins
contain significantly more or significantly less nucleic acid than
one or more other sub-bins, determining that the first bin of
interest harbors a partial aneuploidy.
19. The computer program product of claim 17, wherein analyzing the
tag information for the first bin of interest comprises: dividing
the sequence for the first bin of interest into a plurality of
sub-bins; determining whether any of said sub-bins contains
significantly more or significantly less nucleic acid than one or
more other sub-bins; and if none of said sub-bins contain
significantly more or significantly less nucleic acid than one or
more other sub-bins, determining that the fetus is a mosaic.
20. The system of claim 14, wherein the comparing the first fetal
fraction value and the second fetal fraction value comprises
determining whether the two fetal fraction values are approximately
equal.
21. The system of claim 20, the program instructions further
comprising instructions for determining that the two fetal fraction
values are approximately equal, and thereby determining that the
ploidy assumption of the second method is true.
22. The system of claim 20, the program instructions further
comprising instructions for determining that the two fetal fraction
values are not approximately equal, and analyzing the tag
information for the first bin of interest to determine whether (i)
the first bin of interest harbors a partial aneuploidy, or (ii) the
fetus is a mosaic.
23. The system of claim 22, wherein analyzing the tag information
for the first bin of interest comprises: dividing the sequence for
the first bin of interest into a plurality of sub-bins; determining
whether any of said sub-bins contains significantly more or
significantly less nucleic acid than one or more other sub-bins;
and if any of said plurality of sub-bins contain significantly more
or significantly less nucleic acid than one or more other sub-bins,
determining that the first bin of interest harbors a partial
aneuploidy.
24. The system of claim 22, wherein analyzing the tag information
for the first bin of interest comprises: dividing the sequence for
the first bin of interest into a plurality of sub-bins; determining
whether any of said sub-bins contains significantly more or
significantly less nucleic acid than one or more other sub-bins;
and if none of said sub-bins contain significantly more or
significantly less nucleic acid than one or more other sub-bins,
determining that the fetus is a mosaic.
Description
BACKGROUND
One of the critical endeavors in human medical research is the
discovery of genetic abnormalities that produce adverse health
consequences. In many cases, specific genes and/or critical
diagnostic markers have been identified in portions of the genome
that are present at abnormal copy numbers. For example, in prenatal
diagnosis, extra or missing copies of whole chromosomes are
frequently occurring genetic lesions. In cancer, deletion or
multiplication of copies of whole chromosomes or chromosomal
segments, and higher level amplifications of specific regions of
the genome, are common occurrences.
Most information about copy number variation has been provided by
cytogenetic resolution that has permitted recognition of structural
abnormalities. Conventional procedures for genetic screening and
biological dosimetry have utilized invasive procedures e.g.
amniocentesis, to obtain cells for the analysis of karyotypes.
Recognizing the need for more rapid testing methods that do not
require cell culture, fluorescence in situ hybridization (FISH),
quantitative fluorescence PCR (QF-PCR) and array-Comparative
Genomic Hybridization (array-CGH) have been developed as
molecular-cytogenetic methods for the analysis of copy number
variations.
The advent of technologies that allow for sequencing entire genomes
in relatively short time, and the discovery of circulating
cell-free DNA (cfDNA) have provided the opportunity to compare
genetic material originating from one chromosome to be compared to
that of another without the risks associated with invasive sampling
methods. However, the limitations of the existing methods, which
include insufficient sensitivity stemming from the limited levels
of cfDNA, and the sequencing bias of the technology stemming from
the inherent nature of genomic information, underlie the continuing
need for noninvasive methods that would provide any or all of the
specificity, sensitivity, and applicability, to reliably diagnose
copy number changes in a variety of clinical settings.
Embodiments disclosed herein fulfill some of the above needs and in
particular offers an advantage in providing a reliable method that
is applicable at least to the practice of noninvasive prenatal
diagnostics, and to the diagnosis and monitoring of metastatic
progression in cancer patients.
SUMMARY
Methods are provided for determining copy number variations (CNV)
of a sequence of interest in a test sample that comprises a mixture
of nucleic acids that are known or are suspected to differ in the
amount of one or more sequence of interest. The method comprises a
statistical approach that accounts for accrued variability stemming
from process-related, interchromosomal and inter-sequencing
variability. The method is applicable to determining CNV of any
fetal aneuploidy, and CNVs known or suspected to be associated with
a variety of medical conditions. CNV that can be determined
according to the present method include trisomies and monosomies of
any one or more of chromosomes 1-22, X and Y, other chromosomal
polysomies, and deletions and/or duplications of segments of any
one or more of the chromosomes, which can be detected by sequencing
only once the nucleic acids of a test sample. Any aneuploidy can be
determined from sequencing information that is obtained by
sequencing only once the nucleic acids of a test sample.
In one embodiment, a method is provided for determining the
presence or absence of any four or more different complete fetal
chromosomal aneuploidies in a maternal test sample comprising fetal
and maternal nucleic acid molecules. The steps of the method
comprise (a) obtaining sequence information for the fetal and
maternal nucleic acids in the maternal test sample; (b) using the
sequence information to identify a number of sequence tags for each
of any four or more chromosomes of interest selected from
chromosomes 1-22, X and Y and to identify a number of sequence tags
for a normalizing chromosome sequence for each of the any four or
more chromosomes of interest; (c) using the number of sequence tags
identified for each of the any four or more chromosomes of interest
and the number of sequence tags identified for each normalizing
chromosome to calculate a single chromosome dose for each of the
any four or more chromosomes of interest; and (d) comparing each of
the single chromosome doses for each of the any four or more
chromosomes of interest to a threshold value for each of the four
or more chromosomes of interest, and thereby determining the
presence or absence of any four or more complete different fetal
chromosomal aneuploidies in the maternal test sample. Step (a) can
comprise sequencing at least a portion of the nucleic acid
molecules of a test sample to obtain said sequence information for
the fetal and maternal nucleic acid molecules of the test sample.
In some embodiments, step (c) comprises calculating a single
chromosome dose for each of the chromosomes of interest as the
ratio of the number of sequence tags identified for each of the
chromosomes of interest and the number of sequence tags identified
for the normalizing chromosome sequence for each of the chromosomes
of interest. In some other embodiments, step (c) comprises (i)
calculating a sequence tag density ratio for each of the
chromosomes of interest, by relating the number of sequence tags
identified for each of the chromosomes of interest in step (b) to
the length of each of the chromosomes of interest; (ii) calculating
a sequence tag density ratio for each normalizing chromosome
sequence by relating the number of sequence tags identified for the
sequence in step (b) to the length of each normalizing chromosome;
and (iii) using the sequence tag density ratios calculated in steps
(i) and (ii) to calculate a single chromosome dose for each of the
chromosomes of interest, wherein the chromosome dose is calculated
as the ratio of the sequence tag density ratio for each of the
chromosomes of interest and the sequence tag density ratio for the
normalizing chromosome sequence for each of the chromosomes of
interest.
In another embodiment, a method is provided for determining the
presence or absence of any four or more different complete fetal
chromosomal aneuploidies in a maternal test sample comprising fetal
and maternal nucleic acid molecules. The steps of the method
comprise (a) obtaining sequence information for the fetal and
maternal nucleic acids in the maternal test sample; (b) using the
sequence information to identify a number of sequence tags for each
of any four or more chromosomes of interest selected from
chromosomes 1-22, X and Y and to identify a number of sequence tags
for a normalizing chromosome sequence for each of the any four or
more chromosomes of interest; (c) using the number of sequence tags
identified for each of the any four or more chromosomes of interest
and the number of sequence tags identified for each normalizing
chromosome to calculate a single chromosome dose for each of the
any four or more chromosomes of interest; and (d) comparing each of
the single chromosome doses for each of the any four or more
chromosomes of interest to a threshold value for each of the four
or more chromosomes of interest, and thereby determining the
presence or absence of any four or more complete different fetal
chromosomal aneuploidies in the maternal test sample, wherein the
any four or more chromosomes of interest selected from chromosomes
1-22, X, and Y comprise at least twenty chromosomes selected from
chromosomes 1-22, X, and Y, and wherein the presence or absence of
at least twenty different complete fetal chromosomal aneuploidies
is determined. Step (a) can comprise sequencing at least a portion
of the nucleic acid molecules of a test sample to obtain said
sequence information for the fetal and maternal nucleic acid
molecules of the test sample. In some embodiments, step (c)
comprises calculating a single chromosome dose for each of the
chromosomes of interest as the ratio of the number of sequence tags
identified for each of the chromosomes of interest and the number
of sequence tags identified for the normalizing chromosome sequence
for each of the chromosomes of interest. In some other embodiments,
step (c) comprises (i) calculating a sequence tag density ratio for
each of the chromosomes of interest, by relating the number of
sequence tags identified for each of the chromosomes of interest in
step (b) to the length of each of the chromosomes of interest; (ii)
calculating a sequence tag density ratio for each normalizing
chromosome sequence by relating the number of sequence tags
identified for the normalizing chromosome sequence in step (b) to
the length of each normalizing chromosome; and (iii) using the
sequence tag density ratios calculated in steps (i) and (ii) to
calculate a single chromosome dose for each of the chromosomes of
interest, wherein the chromosome dose is calculated as the ratio of
the sequence tag density ratio for each of the chromosomes of
interest and the sequence tag density ratio for the normalizing
chromosome sequence for each of the chromosomes of interest.
In another embodiment, a method is provided for determining the
presence or absence of any four or more different complete fetal
chromosomal aneuploidies in a maternal test sample comprising fetal
and maternal nucleic acid molecules. The steps of the method
comprise (a) obtaining sequence information for the fetal and
maternal nucleic acids in the maternal test sample; (b) using the
sequence information to identify a number of sequence tags for each
of any four or more chromosomes of interest selected from
chromosomes 1-22, X and Y and to identify a number of sequence tags
for a normalizing chromosome sequence for each of the any four or
more chromosomes of interest; (c) using the number of sequence tags
identified for each of the any four or more chromosomes of interest
and the number of sequence tags identified for each normalizing
chromosome sequence to calculate a single chromosome dose for each
of the any four or more chromosomes of interest; and (d) comparing
each of the single chromosome doses for each of the any four or
more chromosomes of interest to a threshold value for each of the
four or more chromosomes of interest, and thereby determining the
presence or absence of any four or more complete different fetal
chromosomal aneuploidies in the maternal test sample, wherein the
any four or more chromosomes of interest selected from chromosomes
1-22, X, and Y is all of chromosomes 1-22, X, and Y, and wherein
the presence or absence of complete fetal chromosomal aneuploidies
of all of chromosomes 1-22, X, and Y is determined. Step (a) can
comprise sequencing at least a portion of the nucleic acid
molecules of a test sample to obtain said sequence information for
the fetal and maternal nucleic acid molecules of the test sample.
In some embodiments, step (c) comprises calculating a single
chromosome dose for each of the chromosomes of interest as the
ratio of the number of sequence tags identified for each of the
chromosomes of interest and the number of sequence tags identified
for the normalizing chromosome sequence for each of the chromosomes
of interest. In some other embodiments, step (c) comprises (i)
calculating a sequence tag density ratio for each of the
chromosomes of interest, by relating the number of sequence tags
identified for each of the chromosomes of interest in step (b) to
the length of each of the chromosomes of interest; (ii) calculating
a sequence tag density ratio for each normalizing chromosome
sequence by relating the number of sequence tags identified for the
normalizing chromosome sequence in step (b) to the length of each
normalizing chromosome; and (iii) using the sequence tag density
ratios calculated in steps (i) and (ii) to calculate a single
chromosome dose for each of the chromosomes of interest, wherein
the chromosome dose is calculated as the ratio of the sequence tag
density ratio for each of the chromosomes of interest and the
sequence tag density ratio for the normalizing chromosome sequence
for each of the chromosomes of interest.
In any of the embodiments above, the normalizing chromosome
sequence may be a single chromosome selected from chromosomes 1-22,
X, and Y. Alternatively, the normalizing chromosome sequence may be
a group of chromosomes selected from chromosomes 1-22, X, and
Y.
In another embodiment, a method is provided for determining the
presence or absence of any one or more different complete fetal
chromosomal aneuploidies in a maternal test sample comprising fetal
and maternal nucleic acids. The steps of the method comprise: (a)
obtaining sequence information for the fetal and maternal nucleic
acids in the sample; (b) using the sequence information to identify
a number of sequence tags for each of any one or more chromosomes
of interest selected from chromosomes 1-22, X and Y and to identify
a number of sequence tags for a normalizing segment sequence for
each of any one or more chromosomes of interest; (c) using the
number of sequence tags identified for each of any one or more
chromosomes of interest and the number of sequence tags identified
for the normalizing segment sequence to calculate a single
chromosome dose for each of any one or more chromosomes of
interest; and (d) comparing each of the single chromosome doses for
each of any one or more chromosomes of interest to a threshold
value for each of the one or more chromosomes of interest, and
thereby determining the presence or absence of one or more
different complete fetal chromosomal aneuploidies in the sample.
Step (a) can comprise sequencing at least a portion of the nucleic
acid molecules of a test sample to obtain said sequence information
for the fetal and maternal nucleic acid molecules of the test
sample.
In some embodiments, step (c) comprises calculating a single
chromosome dose for each of the chromosomes of interest as the
ratio of the number of sequence tags identified for each of the
chromosomes of interest and the number of sequence tags identified
for the normalizing segment sequence for each of the chromosomes of
interest. In some other embodiments, step (c) comprises (i)
calculating a sequence tag density ratio for each of chromosomes of
interest, by relating the number of sequence tags identified for
each chromosomes of interest in step (b) to the length of each of
the chromosomes of interest; (ii) calculating a sequence tag
density ratio for each normalizing segment sequence by relating the
number of sequence tags identified for the normalizing segment
sequence in step (b) to the length of each the normalizing
chromosomes; and (iii) using the sequence tag density ratios
calculated in steps (i) and (ii) to calculate a single chromosome
dose for each of said chromosomes of interest, wherein said
chromosome dose is calculated as the ratio of the sequence tag
density ratio for each of the chromosomes of interest and the
sequence tag density ratio for the normalizing segment sequence for
each of the chromosomes of interest.
In another embodiment, a method is provided for determining the
presence or absence of any one or more different complete fetal
chromosomal aneuploidies in a maternal test sample comprising fetal
and maternal nucleic acids. The steps of the method comprise: (a)
obtaining sequence information for the fetal and maternal nucleic
acids in the sample; (b) using the sequence information to identify
a number of sequence tags for each of any one or more chromosomes
of interest selected from chromosomes 1-22, X and Y and to identify
a number of sequence tags for a normalizing segment sequence for
each of any one or more chromosomes of interest; (c) using the
number of sequence tags identified for each of any one or more
chromosomes of interest and the number of sequence tags identified
for the normalizing segment sequence to calculate a single
chromosome dose for each of any one or more chromosomes of
interest; and (d) comparing each of the single chromosome doses for
each of any one or more chromosomes of interest to a threshold
value for each of the one or more chromosomes of interest, and
thereby determining the presence or absence of one or more
different complete fetal chromosomal aneuploidies in the sample,
wherein the any one or more chromosomes of interest selected from
chromosomes 1-22, X, and Y comprise at least twenty chromosomes
selected from chromosomes 1-22, X, and Y, and wherein the presence
or absence of at least twenty different complete fetal chromosomal
aneuploidies is determined. Step (a) can comprise sequencing at
least a portion of the nucleic acid molecules of a test sample to
obtain said sequence information for the fetal and maternal nucleic
acid molecules of the test sample. In some embodiments, step (c)
comprises calculating a single chromosome dose for each of the
chromosomes of interest as the ratio of the number of sequence tags
identified for each of the chromosomes of interest and the number
of sequence tags identified for the normalizing segment sequence
for each of the chromosomes of interest. In some other embodiments,
step (c) comprises (i) calculating a sequence tag density ratio for
each of chromosomes of interest, by relating the number of sequence
tags identified for each chromosomes of interest in step (b) to the
length of each of the chromosomes of interest; (ii) calculating a
sequence tag density ratio for each normalizing segment sequence by
relating the number of sequence tags identified for the normalizing
segment sequence in step (b) to the length of each the normalizing
chromosomes; and (iii) using the sequence tag density ratios
calculated in steps (i) and (ii) to calculate a single chromosome
dose for each of said chromosomes of interest, wherein said
chromosome dose is calculated as the ratio of the sequence tag
density ratio for each of the chromosomes of interest and the
sequence tag density ratio for the normalizing segment sequence for
each of the chromosomes of interest.
In another embodiment, a method is provided for determining the
presence or absence of any one or more different complete fetal
chromosomal aneuploidies in a maternal test sample comprising fetal
and maternal nucleic acids. The steps of the method comprise: (a)
obtaining sequence information for the fetal and maternal nucleic
acids in the sample; (b) using the sequence information to identify
a number of sequence tags for each of any one or more chromosomes
of interest selected from chromosomes 1-22, X and Y and to identify
a number of sequence tags for a normalizing segment sequence for
each of any one or more chromosomes of interest; (c) using the
number of sequence tags identified for each of any one or more
chromosomes of interest and the number of sequence tags identified
for the normalizing segment sequence to calculate a single
chromosome dose for each of any one or more chromosomes of
interest; and (d) comparing each of the single chromosome doses for
each of any one or more chromosomes of interest to a threshold
value for each of the one or more chromosomes of interest, and
thereby determining the presence or absence of one or more
different complete fetal chromosomal aneuploidies in the sample,
wherein the any one or more chromosomes of interest selected from
chromosomes 1-22, X, and Y is all of chromosomes 1-22, X, and Y,
and wherein the presence or absence of complete fetal chromosomal
aneuploidies of all of chromosomes 1-22, X, and Y is determined.
Step (a) can comprise sequencing at least a portion of the nucleic
acid molecules of a test sample to obtain said sequence information
for the fetal and maternal nucleic acid molecules of the test
sample. In some embodiments, step (c) comprises calculating a
single chromosome dose for each of the chromosomes of interest as
the ratio of the number of sequence tags identified for each of the
chromosomes of interest and the number of sequence tags identified
for the normalizing segment sequence for each of the chromosomes of
interest. In some other embodiments, step (c) comprises (i)
calculating a sequence tag density ratio for each of chromosomes of
interest, by relating the number of sequence tags identified for
each chromosomes of interest in step (b) to the length of each of
the chromosomes of interest; (ii) calculating a sequence tag
density ratio for each normalizing segment sequence by relating the
number of sequence tags identified for the normalizing segment
sequence in step (b) to the length of each the normalizing
chromosomes; and (iii) using the sequence tag density ratios
calculated in steps (i) and (ii) to calculate a single chromosome
dose for each of said chromosomes of interest, wherein said
chromosome dose is calculated as the ratio of the sequence tag
density ratio for each of the chromosomes of interest and the
sequence tag density ratio for the normalizing segment sequence for
each of the chromosomes of interest.
In any one of the embodiments above, the different complete
chromosomal aneuploidies are selected from complete chromosomal
trisomies, complete chromosomal monosomies and complete chromosomal
polysomies. The different complete chromosomal aneuploidies are
selected from complete aneuploidies of any one of chromosome 1-22,
X, and Y. For example, the said different complete fetal
chromosomal aneuploidies are selected from trisomy 2, trisomy 8,
trisomy 9, trisomy 20, trisomy 21, trisomy 13, trisomy 16, trisomy
18, trisomy 22, 47,XXX, 47, XYY, and monosomy X.
In any one of the embodiments above, steps (a)-(d) are repeated for
test samples from different maternal subjects, and the method
comprises determining the presence or absence of any four or more
different complete fetal chromosomal aneuploidies in each of the
test samples.
In any one of the embodiments above, the method can further
comprise calculating a normalized chromosome value (NCV), wherein
the NCV relates the chromosome dose to the mean of the
corresponding chromosome dose in a set of qualified samples as:
.mu..sigma. ##EQU00001## where {circumflex over (.mu.)}.sub.j and
{circumflex over (.sigma.)}.sub.j are the estimated mean and
standard deviation, respectively, for the j-th chromosome dose in a
set of qualified samples, and x.sub.ij is the observed j-th
chromosome dose for test sample i.
In another embodiment, a method is provided for determining the
presence or absence of different partial fetal chromosomal
aneuploidies in a maternal test sample comprising fetal and
maternal nucleic acids. The steps of the method comprise: (a)
obtaining sequence information for the fetal and maternal nucleic
acids in the sample; (b) using the sequence information to identify
a number of sequence tags for each of any one or more segments of
any one or more chromosomes of interest selected from chromosomes
1-22, X, and Y and to identify a number of sequence tags for a
normalizing segment sequence for each of any one or more segments
of any one or more chromosomes of interest; (c) using the number of
sequence tags identified for each of any one or more segments of
any one or more chromosomes of interest and said number of sequence
tags identified for the normalizing segment sequence to calculate a
single segment dose for each of said any one or more segments of
any one or more chromosomes of interest; and (d) comparing each of
the single segment doses for each of any one or more segments of
any one or more chromosomes of interest to a threshold value for
each of any one or more chromosomal segments of any one or more
chromosome of interest, and thereby determining the presence or
absence of one or more different partial fetal chromosomal
aneuploidies in the sample. Step (a) can comprise sequencing at
least a portion of the nucleic acid molecules of a test sample to
obtain said sequence information for the fetal and maternal nucleic
acid molecules of the test sample.
In some embodiments, step (c) comprises calculating a single
segment dose for each of any one or more segments of any one or
more chromosomes of interest as the ratio of the number of sequence
tags identified for each of any one or more segments of any one or
more chromosomes of interest and the number of sequence tags
identified for the normalizing segment sequence for each of the any
one or more segments of any one or more chromosomes of interest. In
some other embodiments, step (c) comprises (i) calculating a
sequence tag density ratio for each of segment of interest, by
relating the number of sequence tags identified for each segment of
interest in step (b) to the length of each of the segment of
interest; (ii) calculating a sequence tag density ratio for each
normalizing segment sequence by relating the number of sequence
tags identified for the normalizing segment sequence in step (b) to
the length of each the normalizing segment sequence; and (iii)
using the sequence tag density ratios calculated in steps (i) and
(ii) to calculate a single segment dose for each segment of
interest, wherein the segment dose is calculated as the ratio of
the sequence tag density ratio for each of the segments of interest
and the sequence tag density ratio for the normalizing segment
sequence for each of the segments of interest. The method can
further comprise calculating a normalized segment value (NSV),
wherein the NSV relates said segment dose to the mean of the
corresponding segment dose in a set of qualified samples as:
.mu..sigma. ##EQU00002## where {circumflex over (.mu.)}.sub.j and
{circumflex over (.sigma.)}.sub.j are the estimated mean and
standard deviation, respectively, for the j-th segment dose in a
set of qualified samples, and x.sub.ij is the observed j-th segment
dose for test sample i.
In embodiments of the method described whereby a chromosome dose or
a segment dose is determined using a normalizing segment sequence,
the normalizing segment sequence may be a single segment of any one
or more of chromosomes 1-22, X, and Y. Alternatively, the
normalizing segment sequence may be a group of segments of any one
or more of chromosomes 1-22, X, and Y.
Steps (a)-(d) of the method for determining the presence or absence
of a partial fetal chromosomal aneuploidy are repeated for test
samples from different maternal subjects, and the method comprises
determining the presence or absence of different partial fetal
chromosomal aneuploidies in each of said samples. Partial fetal
chromosomal aneuploidies that can be determined according to the
method include partial aneuploidies of any segment of any
chromosome. The partial aneuploidies can be selected from partial
duplications, partial multiplications, partial insertions and
partial deletions. Examples of partial aneuploidies that can be
determined according to the method include partial monosomy of
chromosome 1, partial monosomy of chromosome 4, partial monosomy of
chromosome 5, partial monosomy of chromosome 7, partial monosomy of
chromosome 11, partial monosomy of chromosome 15, partial monosomy
of chromosome 17, partial monosomy of chromosome 18, and partial
monosomy of chromosome 22.
In any one of the embodiments described above, the test sample may
be a maternal sample selected from blood, plasma, serum, urine and
saliva samples. In any one of the embodiments, the test sample is
may be plasma sample. The nucleic acid molecules of the maternal
sample are a mixture of fetal and maternal cell-free DNA molecules.
Sequencing of the nucleic acids can be performed using next
generation sequencing (NGS). In some embodiments, sequencing is
massively parallel sequencing using sequencing-by-synthesis with
reversible dye terminators. In other embodiments, sequencing is
sequencing-by-ligation. In yet other embodiments, sequencing is
single molecule sequencing. Optionally, an amplification step is
performed prior to sequencing.
In another embodiment, a method is provided for determining the
presence or absence of any twenty or more different complete fetal
chromosomal aneuploidies in a maternal plasma test sample
comprising a mixture of fetal and maternal cell-free DNA molecules.
The steps of the method comprise: (a) sequencing at least a portion
of the cell-free DNA molecules to obtain sequence information for
the fetal and maternal cell-free DNA molecules in the sample; (b)
using the sequence information to identify a number of sequence
tags for each of any twenty or more chromosomes of interest
selected from chromosomes 1-22, X, and Y and to identify a number
of sequence tags for a normalizing chromosome for each of said
twenty or more chromosomes of interest; (c) using the number of
sequence tags identified for each of the twenty or more chromosomes
of interest and the number of sequence tags identified for each
normalizing chromosome to calculate a single chromosome dose for
each of the twenty or more chromosomes of interest; and (d)
comparing each of the single chromosome doses for each of the
twenty or more chromosomes of interest to a threshold value for
each of the twenty or more chromosomes of interest, and thereby
determining the presence or absence of any twenty or more different
complete fetal chromosomal aneuploidies in the sample.
In another embodiment, the invention provides a method for
identifying copy number variation (CNV) of a sequence of interest
e.g. a clinically relevant sequence, in a test sample comprising
the steps of: (a) obtaining a test sample and a plurality of
qualified samples, said test sample comprising test nucleic acid
molecules and said plurality of qualified samples comprising
qualified nucleic acid molecules; (b) obtaining sequence
information for said fetal and maternal nucleic acids in said
sample; (c) based on said sequencing of said qualified nucleic acid
molecules, calculating a qualified sequence dose for said qualified
sequence of interest in each of said plurality of qualified
samples, wherein said calculating a qualified sequence dose
comprises determining a parameter for said qualified sequence of
interest and at least one qualified normalizing sequence; (d) based
on said qualified sequence dose, identifying at least one qualified
normalizing sequence, wherein said at least one qualified
normalizing sequence has the smallest variability and/or the
greatest differentiability in sequence dose in said plurality of
qualified samples; (e) based on said sequencing of said nucleic
acid molecules in said test sample, calculating a test sequence
dose for said test sequence of interest, wherein said calculating a
test sequence dose comprises determining a parameter for said test
sequence of interest and at least one normalizing test sequence,
and wherein said at least one normalizing test sequence corresponds
to said at least one qualified normalizing sequence; (f) comparing
said test sequence dose to at least one threshold value; and (g)
assessing said copy number variation of said sequence of interest
in said test sample based on the outcome of step (f). In one
embodiment, the parameter for said qualified sequence of interest
and at least one qualified normalizing sequence relates the number
of sequence tags mapped to said qualified sequence of interest to
the number of tags mapped to said qualified normalizing sequence,
and wherein said parameter for said test sequence of interest and
at least one normalizing test sequence relates the number of
sequence tags mapped to said test sequence of interest to the
number of tags mapped to said normalizing test sequence. In some
embodiments, step (b) comprises sequencing at least a portion of
the qualified and test nucleic acid molecules, wherein sequencing
comprises providing a plurality of mapped sequence tags for a test
and a qualified sequence of interest, and for at least one test and
at least one qualified normalizing sequence; sequencing at least a
portion of said nucleic acid molecules of the test sample to obtain
the sequence information for the fetal and maternal nucleic acid
molecules of the test sample. In some embodiments, the sequencing
step is performed using next generation sequencing method. In some
embodiments, the sequencing method may be a massively parallel
sequencing method that uses sequencing-by-synthesis with reversible
dye terminators. In other embodiments, the sequencing method is
sequencing-by-ligation. In some embodiments, sequencing comprises
an amplification. In other embodiments, sequencing is single
molecule sequencing. The CNV of a sequence of interest is an
aneuploidy, which can be a chromosomal or a partial aneuploidy. In
some embodiments, the chromosomal aneuploidy is selected from
trisomy 2, trisomy 8, trisomy 9, trisomy 20, trisomy 16, trisomy
21, trisomy 13, trisomy 18, trisomy 22, klinefelter's syndrome,
47,XXX, 47,XYY, and monosomy X. In other embodiments, the partial
aneuploidy is a partial chromosomal deletion or a partial
chromosomal insertion. In some embodiments, the CNV identified by
the method is a chromosomal or partial aneuploidy associated with
cancer. In some embodiments, the test and qualified sample are
biological fluid samples e.g. plasma samples, obtained from a
pregnant subject such as a pregnant human subject. In other
embodiments, a test and qualified biological fluid samples e.g.
plasma samples, are obtained from a subject that is known or is
suspected of having cancer.
Some methods for determining the presence or absence of a fetal
chromosomal aneuploidy in a maternal test sample may include the
following operations: (a) providing sequence reads from fetal and
maternal nucleic acids in the maternal test sample, wherein the
sequence reads are provided in an electronic format; (b) aligning
the sequence reads to one or more chromosome reference sequences
using a computing apparatus and thereby providing sequence tags
corresponding to the sequence reads; (c) computationally
identifying a number of those sequence tags that are from one or
more chromosomes of interest or chromosome segments of interest and
computationally identifying a number of those sequence tags that
are from at least one normalizing chromosome sequence or
normalizing chromosome segment sequence for each of the one or more
chromosomes of interest or chromosome segments of interest; (d)
computationally calculating, using said number of sequence tags
identified for each of said one or more chromosomes of interest or
chromosome segments of interest and said number of sequence tags
identified for each said normalizing chromosome sequence or
normalizing chromosome segment sequence, a single chromosome or
segment dose for each of said one or more chromosomes of interest
or chromosome segments of interest; and (e) comparing, using said
computing apparatus, each of said single chromosome doses for each
of one or more chromosomes of interest or chromosome segments of
interest to a corresponding threshold value for each of said one or
more chromosomes of interest or chromosome segments of interest,
and thereby determining the presence or absence of at least one
fetal aneuploidy in said test sample. In certain implementations,
the number of sequence tags identified for each of the one or more
chromosomes of interest or chromosome segments of interest is at
least about 10,000, or at least about 100,000. The disclosed
embodiments also provide a computer program product including a
non-transitory computer readable medium on which is provided
program instructions for performing the recited operations and
other computational operations described herein.
In some embodiments, the chromosome reference sequences have
excluded regions that are present naturally in chromosomes but
which do not contribute to the number of sequence tags for any
chromosome or chromosome segment. In some embodiments, a method
additionally includes (i) determining whether a read under
consideration aligns to a site on a chromosome reference sequence
where another read from the test sample previous aligned; and (ii)
determining whether to include the read under consideration in the
number of sequence tags for a chromosome of interest or a
chromosome segment of interest. The chromosome reference sequence
may be stored on a computer readable medium.
In some embodiments, a method additionally includes sequencing at
least a portion of said nucleic acid molecules of said maternal
test sample to obtain said sequence information for said fetal and
maternal nucleic acid molecules of said test sample. The sequencing
may involve massively parallel sequencing on maternal and fetal
nucleic acids from the maternal test sample to produce the sequence
reads.
In some embodiments, a method further includes automatically
recording, using a processor, the presence or absence of a fetal
chromosomal aneuploidy as determined in (d) in a patient medical
record for a human subject providing the maternal test sample. The
recording may include recording chromosome doses and/or a diagnosis
based said chromosome doses in a computer-readable medium. In some
cases, the patient medical record is maintained by a laboratory,
physician's office, a hospital, a health maintenance organization,
an insurance company, or a personal medical record website. A
method may further include prescribing, initiating, and/or altering
treatment of a human subject from whom the maternal test sample was
taken. Additionally or alternatively, the method may include
ordering and/or performing one or more additional tests.
Some methods disclosed herein identify normalizing chromosome
sequences or normalizing chromosome segment sequences for a
chromosome or chromosome segment of interest. Some such methods
include the following operations: (a) providing a plurality of
qualified samples for the chromosome or chromosome segment of
interest; (b) repeatedly calculating chromosome doses for the
chromosome or chromosome segment of interest using multiple
potential normalizing chromosome sequences or normalizing
chromosome segment sequences, wherein the repeated calculation is
performed with a computing apparatus; and (c) selecting a
normalizing chromosome sequence or normalizing chromosome segment
sequence alone or in a combination giving a smallest variability
and/or a large differentiability in calculated doses for the
chromosome or chromosome segment of interest.
A selected normalizing chromosome sequence or normalizing
chromosome segment sequence may be part of a combination of
normalizing chromosome sequences or normalizing chromosome segment
sequences or it may be provided alone, and not in combination with
other normalizing chromosome sequences or normalizing chromosome
segment sequences.
The disclosed embodiments provide a method for classifying a copy
number variation in a fetal genome. The operations of the method
include: (a) receiving sequence reads from fetal and maternal
nucleic acids in a maternal test sample, wherein the sequence reads
are provided in an electronic format; (b) aligning the sequence
reads to one or more chromosome reference sequences using a
computing apparatus and thereby providing sequence tags
corresponding to the sequence reads; (c) computationally
identifying a number of those sequence tags that are from one or
more chromosomes of interest by using the computing apparatus and
determining that a first chromosome of interest in the fetus
harbors a copy number variation; (d) calculating a first fetal
fraction value by a first method that does not use information from
the tags from the first chromosome of interest; (e) calculating a
second fetal fraction value by a second method that uses
information from the tags from the first chromosome; and (f)
comparing the first fetal fraction value and the second fetal
fraction value and using the comparison to classify the copy number
variation of the first chromosome. In some embodiments, the method
further includes sequencing cell free DNA from the maternal test
sample to provide the sequence reads. In some embodiments, the
method further includes obtaining the maternal test sample from a
pregnant organism. In some embodiments, operation (b) includes
using the computing apparatus to align at least about 1 million
reads. In some embodiments, operation (f) can include determining
whether the two fetal fraction values are approximately equal.
In some embodiments, operation (f) can further include determining
that the two fetal fraction values are approximately equal, and
thereby determining that a ploidy assumption implicit in the second
method is true. In some embodiments, the ploidy assumption implicit
in the second method is that the first chromosome of interest has a
complete chromosomal aneuploidy. In some of these embodiments, the
complete chromosomal aneuploidy of the first chromosome of interest
is a monosomy or a trisomy.
In some embodiments, operation (f) can include determining whether
the two fetal fraction values are not approximately equal, and
further include analyzing the tag information for the first
chromosome of interest to determine whether (i) the first
chromosome of interest harbors a partial aneuploidy, or (ii) the
fetus is a mosaic.
In some embodiments, this operation can also include binning the
sequence for the first chromosome of interest into a plurality of
portions; determining whether any of said portions contains
significantly more or significantly less nucleic acid than one or
more other portions; and if any of said portions contain
significantly more or significantly less nucleic acid than one or
more other portions, determining that the first chromosome of
interest harbors a partial aneuploidy. In one embodiment, this
operation can further include determining that a portion of the
first chromosome of interest containing significantly more or
significantly less nucleic acid than one or more other portions
harbors the partial aneuploidy.
In one embodiments, operation (f) can also include binning the
sequence for the first chromosome of interest into a plurality of
portions; determining whether any of said portions contains
significantly more or significantly less nucleic acid than one or
more other portions; and if none of said portions contain
significantly more or significantly less nucleic acid than one or
more other portions, determining that the fetus is a mosaic.
Operation (e) can include: (a) calculating the number of sequence
tags from the first chromosome of interest and at least one
normalizing chromosome sequence to determine a chromosome dose; and
(b) calculating the fetal fraction value from the chromosome dose
using the second method. In some embodiments, this operation
further includes calculating a normalized chromosome value (NCV),
wherein the second method uses the normalized chromosome value, and
wherein the NCV relates the chromosome dose to the mean of the
corresponding chromosome dose in a set of qualified samples as:
.times..times..sigma. ##EQU00003## where R.sub.iU and
.sigma..sub.iU are the estimated mean and standard deviation,
respectively, for the i-th chromosome dose in the set of qualified
samples, and R.sub.iA is the chromosome dose calculated for the
chromosome of interest. In another embodiment, operation (d)
further includes that the first method calculates the first fetal
fraction value using information from one or more polymorphisms
exhibiting an allelic imbalance in the fetal and maternal nucleic
acids of the maternal test sample.
In various embodiments, if the first fetal fraction value is not
approximately equal to the second fetal fraction value, the method
further includes (i) determining whether the copy number variation
results from a partial aneuploidy or a mosaic; and (ii) if the copy
number variation results from a partial aneuploidy, determining the
locus of the partial aneuploidy on the first chromosome of
interest. In some embodiments, determining the locus of the partial
aneuploidy on the first chromosome of interest includes
categorizing the sequence tags for the first chromosome of interest
into bins of blocks of nucleic acids in the first chromosome of
interest; and counting the mapped tags in each bin.
Operation (e) can further include calculating the fetal fraction
value by evaluating the following expression:
ff=2.times.NCV.sub.iACV.sub.iU where ff is the second fetal
fraction value, NCV.sub.iA is the normalized chromosome value at
the i-th chromosome in an affected sample, and CV.sub.iU is the
coefficient of variation for doses of the chromosome of interest
determined in the qualified samples.
In any one of the embodiments above, the first chromosome of
interest is selected from a group consisting of chromosomes 1-22,
X, and Y. In any one of the above embodiments, operation (f) can
classify the copy number variation into a classification selected
from the group consisting of complete chromosomal insertions,
complete chromosomal deletions, partial chromosomal duplications,
and partial chromosomal deletions, and mosaics.
The disclosed embodiments also provide a computer program product
including a non-transitory computer readable medium on which is
provided program instructions for classifying a copy number
variation in a fetal genome. The computer program product can
include: (a) code for receiving sequence reads from fetal and
maternal nucleic acids in a maternal test sample, wherein the
sequence reads are provided in an electronic format; (b) code for
aligning the sequence reads to one or more chromosome reference
sequences using a computing apparatus and thereby providing
sequence tags corresponding to the sequence reads; (c) code for
computationally identifying a number of those sequence tags that
are from one or more chromosomes of interest by using the computing
apparatus and determining that a first chromosome of interest in
the fetus harbors a copy number variation; (d) code for calculating
a first fetal fraction value by a first method that does not use
information from the tags from the first chromosome of interest;
(e) code for calculating a second fetal fraction value by a second
method that uses information from the tags from the first
chromosome; and (f) code for comparing the first fetal fraction
value and the second fetal fraction value and using the comparison
to classify the copy number variation of the first chromosome. In
some embodiments, the computer program product includes code for
the various operations and methods in the any of the above
embodiments of the methods disclosed.
The disclosed embodiments also provide a system for classifying a
copy number variation in a fetal genome. The system includes (a) an
interface for receiving at least about 10,000 sequence reads from
fetal and maternal nucleic acids in a maternal test sample, wherein
the sequence reads are provided in an electronic format; (b) memory
for storing, at least temporarily, a plurality of said sequence
reads; (c) a processor designed or configured with program
instructions for: (i) aligning the sequence reads to one or more
chromosome reference sequences and thereby providing sequence tags
corresponding to the sequence reads; (ii) identifying a number of
those sequence tags that are from one or more chromosomes of
interest and determining that a first chromosome of interest in the
fetus harbors a copy number variation; (iii) calculating a first
fetal fraction value by a first method that does not use
information from the tags from the first chromosome of interest;
(iv) calculating a second fetal fraction value by a second method
that uses information from the tags from the first chromosome; and
(v) comparing the first fetal fraction value and the second fetal
fraction value and using the comparison to classify the copy number
variation of the first chromosome. According to various
embodiments, the first chromosome of interest is selected from a
group consisting of chromosomes 1-22, X, and Y. In some
embodiments, the program instructions for (c)(v) includes program
instructions for classifying the copy number variation into a
classification selected from the group consisting of complete
chromosomal insertions, complete chromosomal deletions, partial
chromosomal duplications, and partial chromosomal deletions, and
mosaics. According to various embodiments, the system can include
program instructions for sequencing cell free DNA from the maternal
test sample to provide the sequence reads. According to some
embodiments, the program instructions for operation (c)(i) includes
program instructions for using the computing apparatus to align at
least about 1 million reads.
In some embodiments, the system also includes a sequencer
configured to sequence the fetal and maternal nucleic acids in a
maternal test sample and provide the sequence reads in electronic
format. In various embodiments, the sequencer and the processor are
located in separate facilities and the sequencer and the processor
are linked by a network.
In various embodiments, the system also further includes an
apparatus for taking the maternal test sample from a pregnant
mother. According to some embodiments, the apparatus for taking the
maternal test sample and the processor are located in separate
facilities. In various embodiments, the system also includes an
apparatus for extracting cell free DNA from the maternal test
sample. In some embodiments, the apparatus for extracting cell free
DNA is located in the same facility with the sequencer, and the
apparatus for taking the maternal test sample is located in a
remote facility.
According to some embodiments, the program instructions for
comparing the first fetal fraction value and the second fetal
fraction value also include program instructions for determining
whether the two fetal fraction values are approximately equal.
In some embodiments, the system also includes program instructions
for determining that a ploidy assumption implicit in the second
method is true when the two fetal fraction values are approximately
equal. In some embodiments, the ploidy assumption implicit in the
second method is that the first chromosome of interest has a
complete chromosomal aneuploidy. In some embodiments, the complete
chromosomal aneuploidy of the first chromosome of interest is a
monosomy or a trisomy.
In some embodiments, the system also includes program instructions
for analyzing the tag information for the first chromosome of
interest to determine whether (i) first chromosome of interest
harbors a partial aneuploidy, or (ii) the fetus is a mosaic,
wherein the program instructions for analyzing are configured to
execute when the program instructions for comparing the first fetal
fraction value and the second fetal fraction value indicate that
the two fetal fraction values are not approximately equal. In some
embodiments, the program instructions for analyzing the tag
information for the first chromosome of interest include: program
instructions for binning the sequence for the first chromosome of
interest into a plurality of portions; program instructions for
determining whether any of said portions contains significantly
more or significantly less nucleic acid than one or more other
portions; and program instructions for determining that the first
chromosome of interest harbors a partial aneuploidy if any of said
portions contain significantly more or significantly less nucleic
acid than one or more other portions. In some embodiments, the
system further includes program instructions for determining that a
portion of the first chromosome of interest containing
significantly more or significantly less nucleic acid than one or
more other portions harbors the partial aneuploidy.
In some embodiments, the program instructions for analyzing the tag
information for the first chromosome of interest include: program
instructions for binning the sequence for the first chromosome of
interest into a plurality of portions; program instructions for
determining whether any of said portions contains significantly
more or significantly less nucleic acid than one or more other
portions; and program instructions for determining that the fetus
is a mosaic if none of said portions contain significantly more or
significantly less nucleic acid than one or more other
portions.
According to various embodiments, the system can include program
instructions for the second method of calculating the fetal
fraction value that include: (a) program instructions for
calculating the number of sequence tags from the first chromosome
of interest and at least one normalizing chromosome sequence to
determine a chromosome dose; and (b) program instructions for
calculating the fetal fraction value from the chromosome dose using
the second method.
In some embodiments, the system further includes program
instructions for calculating a normalized chromosome value (NCV),
wherein the program instructions for the second method include
program instructions for using the normalized chromosome value, and
wherein the program instructions for the NCV relate the chromosome
dose to the mean of the corresponding chromosome dose in a set of
qualified samples as:
.times..times..sigma. ##EQU00004##
where R.sub.iU and .sigma..sub.iU are the estimated mean and
standard deviation, respectively, for the i-th chromosome dose in
the set of qualified samples, and R.sub.iA is the chromosome dose
calculated for the chromosome of interest. In various embodiments,
the program instructions for the first method include program
instructions for calculating the first fetal fraction value using
information from one or more polymorphisms exhibiting an allelic
imbalance in the fetal and maternal nucleic acids of the maternal
test sample.
According to various embodiments, the program instructions for the
second method of calculating the fetal fraction value include
program instructions for evaluating the following expression:
ff=2.times.NCV.sub.iACV.sub.iU where ff is the second fetal
fraction value, NCV.sub.iA is the normalized chromosome value at
the i-th chromosome in an affected sample, and CV.sub.iU is the
coefficient of variation for doses of the chromosome of interest
determined in the qualified samples.
According to various embodiments, the system further includes (i)
program instructions for determining whether the copy number
variation results from a partial aneuploidy or a mosaic; and (ii)
program instructions for if the copy number variation results from
a partial aneuploidy, determining the locus of the partial
aneuploidy on the first chromosome of interest, wherein the program
instructions in (i) and (ii) is configured to execute when the
program instructions for comparing the first fetal fraction value
and the second fetal fraction value determine that the first fetal
fraction value is not approximately equal to the second fetal
fraction value.
In some embodiments, program instructions for determining the locus
of the partial aneuploidy on the first chromosome of interest
include program instructions for categorizing the sequence tags for
the first chromosome of interest into bins of blocks of nucleic
acids in the first chromosome of interest; and program instructions
for counting the mapped tags in each bin.
In certain embodiments, methods for identifying the presence of a
cancer and/or an increased risk of a cancer in a mammal (e.g., a
human) are provided where the methods comprise: (a) providing
sequence reads of nucleic acids in a test sample from said mammal,
wherein said test sample may comprise both genomic nucleic acids
from cancerous or precancerous cells and genomic nucleic acids from
constitutive (germline) cells, wherein the sequence reads are
provided in an electronic format; (b) aligning the sequence reads
to one or more chromosome reference sequences using a computing
apparatus and thereby providing sequence tags corresponding to the
sequence reads; (c) computationally identifying a number of
sequence tags from the fetal and maternal nucleic acids for one or
more chromosomes of interest amplification of which or deletions of
which are known to be associated with cancers, or chromosome
segments of interest amplification(s) of which or deletions of
which are known to be associated with cancers, wherein said
chromosome or chromosome segments are selected from chromosomes
1-22, X, and Y and segments thereof and computationally identifying
a number of sequence tags for at least one normalizing chromosome
sequence or normalizing chromosome segment sequence for each of the
one or more chromosomes of interest or chromosome segments of
interest, wherein the number of sequence tags identified for each
of the one or more chromosomes of interest or chromosome segments
of interest is at least about 2,000, or at least about 5,000, or at
least about 10,000; (d) computationally calculating, using said
number of sequence tags identified for each of said one or more
chromosomes of interest or chromosome segments of interest and said
number of sequence tags identified for each said normalizing
chromosome sequence or normalizing chromosome segment sequence, a
single chromosome or segment dose for each of said one or more
chromosomes of interest or chromosome segments of interest; and (e)
comparing, using said computing apparatus, each of said single
chromosome doses for each of one or more chromosomes of interest or
chromosome segments of interest to a corresponding threshold value
for each of said one or more chromosomes of interest or chromosome
segments of interest, and thereby determining the presence or
absence of aneuploidies in said sample, where the presence of said
aneuploidies and/or an increased number of said is an indicator of
the presence and/or increased risk of a cancer. In certain
embodiments, the increased risk is as compared to the same subject
at a different time (e.g., earlier in time), as compared to a
reference population (e.g., optionally adjusted for gender, and/or
ethnicity, and/or age, etc.), as compared to a similar subject
absent exposure to certain risk factors, and the like. In certain
embodiments chromosomes of interest or chromosome segments of
interest comprise whole chromosomes amplifications and/or deletions
of which are known to be associated with a cancer (e.g., as
described herein). In certain embodiments chromosomes of interest
or chromosome segments of interest comprise chromosome segments
amplifications or deletions of which are known to be associated
with one or more cancers. In certain embodiments the chromosome
segments comprise substantially whole chromosome arms (e.g., as
described herein). In certain embodiments the chromosome segments
comprise whole chromosome aneuploidies. In certain embodiments the
whole chromosome aneuploidies comprise a loss, while in certain
other embodiments the whole chromosome aneuploidies comprise a gain
(e.g., a gain or a loss as shown in Table 1). In certain
embodiments the chromosomal segments of interest are substantially
arm-level segments comprising a p arm or a q arm of any one or more
of chromosomes 1-22, X and Y. In certain embodiments the
aneuploidies comprise an amplification of a substantial arm level
segment of a chromosome or a deletion of a substantial arm level
segment of a chromosome. In certain embodiments the chromosomal
segments of interest substantially comprise one or more arms
selected from the group consisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p,
6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p,
17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q, and/or 22q. In certain
embodiments the aneuploidies comprise an amplification of one or
more arms selected from the group consisting of 1q, 3q, 4p, 4q, 5p,
5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q,
16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q, 22q. In certain
embodiments the aneuploidies comprise a deletion of one or more
arms selected from the group consisting of 1p, 3p, 4p, 4q, 5q, 6q,
8p, 8q, 9p, 9q, 10p, 10q, 11p, 11q, 13q, 14q, 15q, 16q, 17p, 17q,
18p, 18q, 19p, 19q, 22q. In certain embodiments the chromosomal
segments of interest are segments that comprise a region and/or a
gene shown in Table 3 and/or Table 5 and/or Table 4 and/or Table 6.
In certain embodiments the aneuploidies comprise an amplification
of a region and/or a gene shown in Table 3 and/or Table 5. In
certain embodiments the aneuploidies comprise a deletion of a
region and/or a gene shown in Table 4 and/or Table 6. In certain
embodiments the chromosomal segments of interest are segments known
to contain one or more oncogenes and/or one or more tumor
suppressor genes. In certain embodiments the aneuploidies comprise
an amplification of one or more regions selected from the group
consisting of 20Q13, 19q12, 1q21-1q23, 8p11-p12, and the ErbB2. In
certain embodiments the aneuploidies comprise an amplification of
one or more regions comprising a gene selected from the group
consisting of MYC, ERBB2 (EFGR), CCND1 (Cyclin D1), FGFR1, FGFR2,
HRAS, KRAS, MYB, MDM2, CCNE, KRAS, MET, ERBB1, CDK4, MYCB, ERBB2,
AKT2, MDM2 and CDK4, and the like. In certain embodiments the
cancer is a cancer selected from the group consisting of leukemia,
ALL, brain cancer, breast cancer, colorectal cancer,
dedifferentiated liposarcoma, esophageal adenocarcinoma, esophageal
squamous cell cancer, GIST, glioma, HCC, hepatocellular cancer,
lung cancer, lung NSC, lung SC, medulloblastoma, melanoma, MPD,
myeloproliferative disorder, cervical cancer, ovarian cancer,
prostate cancer, and renal cancer. In certain embodiments the
biological sample comprise a sample selected from the group
consisting of whole blood, a blood fraction, saliva/oral fluid,
urine, a tissue biopsy, pleural fluid, pericardial fluid, cerebral
spinal fluid, and peritoneal fluid. In certain embodiments the
chromosome reference sequences have excluded regions that are
present naturally in chromosomes but that do not contribute to the
number of sequence tags for any chromosome or chromosome segment.
In certain embodiments the methods further comprise determining
whether a read under consideration aligns and to a site on a
chromosome reference sequence where another read previous aligned;
and determining whether to include the read under consideration in
the number of sequence tags for a chromosome of interest or a
chromosome segment of interest, wherein both determining operations
are performed with the computing apparatus. In various embodiments
the methods further comprise storing in a computer readable medium
(e.g., a non-transitory medium), at least temporarily, sequence
information for said nucleic acids in said sample. In certain
embodiments step (d) comprises computationally calculating a
segment dose for a selected one of segments of interest as the
ratio of the number of sequence tags identified for the selected
segment of interest and the number of sequence tags identified for
a corresponding at least one normalizing chromosome sequence or
normalizing chromosome segment sequence for the selected segment of
interest. In certain embodiments the said one or more chromosome
segments of interest comprise at least 5, or at least 10, or at
least 15, or at least 20, or at least 50, or at least 100 different
segments of interest. In certain embodiments at least 5, or at
least 10, or at least 15, or at least 20, or at least 50, or at
least 100 different aneuploidies are detected. In certain
embodiments at least one normalizing chromosome sequence comprises
one or more chromosomes selected from the group consisting of
chromosomes 1-22, X, and Y. In certain embodiments said at least
one normalizing chromosome sequence comprises for each segment the
chromosome corresponding to the chromosome in which said segment is
located. In certain embodiments the at least one normalizing
chromosome sequence comprises for each segment the chromosome
segment corresponding to the chromosome segment that is being
normalized. In certain embodiments at least one normalizing
chromosome sequence or normalizing chromosome segment sequence is a
chromosome or segment selected for an associated chromosome or
segment of interest by (i) identifying a plurality of qualified
samples for the segment of interest; (ii) repeatedly calculating
chromosome doses for the selected chromosome segment using multiple
potential normalizing chromosome sequences or normalizing
chromosome segment sequences; and (iii) selecting the normalizing
chromosome segment sequence alone or in a combination giving the
smallest variability and/or greatest differentiability in
calculated chromosome doses. In certain embodiments the method
further comprises calculating a normalized segment value (NSV),
wherein said NSV relates said segment dose to the mean of the
corresponding segment dose in a set of qualified samples as
described herein. In certain embodiments the normalizing segment
sequence is a single segment of any one or more of chromosomes
1-22, X, and Y. In certain embodiments the normalizing segment
sequence is a group of segments of any one or more of chromosomes
1-22, X, and Y. In certain embodiments the normalizing segment
comprises substantially one arm of any one or more of chromosomes
1-22, X, and Y. In certain embodiments the method further comprises
sequencing at least a portion of said nucleic acid molecules of
said test sample to obtain said sequence information. In certain
embodiments the sequencing comprises sequencing cell free DNA from
the test sample to provide the sequence information. In certain
embodiments the sequencing comprises sequencing cellar DNA from the
test sample to provide the sequence information. In certain
embodiments the sequencing comprises massively parallel sequencing.
In certain embodiments the method(s) further comprise automatically
recording the presence or absence of an aneuploidy as determined in
(d) in a patient medical record for a human subject providing the
test sample, wherein the recording is performed using the
processor. In certain embodiments the recording comprises recording
the chromosome doses and/or a diagnosis based said chromosome doses
in a computer-readable medium. In various embodiments the patient
medical record is maintained by a laboratory, physician's office, a
hospital, a health maintenance organization, an insurance company,
or a personal medical record website. In certain embodiments the
determination of the presence or absence and/or number of said
aneuploidies comprises a component in a differential diagnosis for
cancer. In certain embodiments the detection of aneuploidies
indicates a positive result and said method further comprises
prescribing, initiating, and/or altering treatment of a human
subject from whom the test sample was taken. In certain embodiments
prescribing, initiating, and/or altering treatment of a human
subject from whom the test sample was taken comprises prescribing
and/or performing further diagnostics to determine the presence
and/or severity of a cancer. In certain embodiments the further
diagnostics comprise screening a sample from said subject for a
biomarker of a cancer, and/or imaging said subject for a cancer. In
certain embodiments when said method indicates the presence of
neoplastic cells in said mammal, treating said mammal, or causing
said mammal to be treated, to remove and/or to inhibit the growth
or proliferation of said neoplastic cells. In certain embodiments
treating the mammal comprises surgically removing the neoplastic
(e.g., tumor) cells. In certain embodiments treating the mammal
comprises performing radiotherapy or causing radiotherapy to be
performed on said mammal to kill the neoplastic cells. In certain
embodiments treating the mammal comprises administering or causing
to be administered to said mammal an anti-cancer drug (e.g.,
matuzumab, erbitux, vectibix, nimotuzumab, matuzumab, panitumumab,
fluorouracil, capecitabine, 5-trifluoromethyl-2'-deoxyuridine,
methotrexate, raltitrexed, pemetrexed, cytosine arabinoside,
6-mercaptopurine, azathioprine, 6-thioguanine, pentostatin,
fludarabine, cladribine, floxuridine, cyclophosphamide, neosar,
ifosfamide, thiotepa, 1,3-bis(2-chloroethyl)-1-nitosourea,
1,-(2-chloroethyl)-3-cyclohexyl-Initrosourea, hexamethylmelamine,
busulfan, procarbazine, dacarbazine, chlorambucil, melphalan,
cisplatin, carboplatin, oxaliplatin, bendamustine, carmustine,
chloromethine, dacarbazine, fotemustine, lomustine, mannosulfan,
nedaplatin, nimustine, prednimustine, ranimustine, satraplatin,
semustine, streptozocin, temozolomide, treosulfan, triaziquone,
triethylene melamine, thiotepa, triplatin tetranitrate,
trofosfamide, uramustine, doxorubicin, daunorubicin, mitoxantrone,
etoposide, topotecan, teniposide, irinotecan, camptosar,
camptothecin, belotecan, rubitecan, vincristine, vinblastine,
vinorelbine, vindesine, paclitaxel, docetaxel, abraxane,
ixabepilone, larotaxel, ortataxel, tesetaxel, vinflunine, imatinib
mesylate, sunitinib malate, sorafenib tosylate, nilotinib
hydrochloride monohydrate/, tasigna, semaxanib, vandetanib,
vatalanib, retinoic acid, a retinoic acid derivative, and the
like).
In another embodiment, a computer program product for use in
determining the presence of a cancer and/or an increased risk of a
cancer in a mammal is provided. The computer program product
typically comprises: (a) code for providing sequence reads of
nucleic acids in a test sample from said mammal, wherein said test
sample may comprise both genomic nucleic acids from cancerous or
precancerous cells and genomic nucleic acids from constitutive
(germline) cells, wherein the sequence reads are provided in an
electronic format; (b) code for aligning the sequence reads to one
or more chromosome reference sequences using a computing apparatus
and thereby providing sequence tags corresponding to the sequence
reads; (c) code for computationally identifying a number of
sequence tags from the fetal and maternal nucleic acids for one or
more chromosomes of interest amplification of which or deletions of
which are known to be associated with cancers, or chromosome
segments of interest amplification of which or deletions of which
are known to be associated with cancers, wherein said chromosome or
chromosome segments are selected from chromosomes 1-22, X, and Y
and segments thereof and computationally identifying a number of
sequence tags for at least one normalizing chromosome sequence or
normalizing chromosome segment sequence for each of the one or more
chromosomes of interest or chromosome segments of interest, wherein
the number of sequence tags identified for each of the one or more
chromosomes of interest or chromosome segments of interest is at
least about 10,000; (d) code for computationally calculating, using
said number of sequence tags identified for each of said one or
more chromosomes of interest or chromosome segments of interest and
said number of sequence tags identified for each said normalizing
chromosome sequence or normalizing chromosome segment sequence, a
single chromosome or segment dose for each of said one or more
chromosomes of interest or chromosome segments of interest; and (e)
code for comparing, using said computing apparatus, each of said
single chromosome doses for each of one or more chromosomes of
interest or chromosome segments of interest to a corresponding
threshold value for each of said one or more chromosomes of
interest or chromosome segments of interest, and thereby
determining the presence or absence of aneuploidies in said sample,
where the presence of said aneuploidies and/or an increased number
of said is an indicator of the presence and/or increased risk of a
cancer. In various embodiments the code provides instructions for
performance of the diagnostic methods as described above (and later
herein).
Methods of treating a subject for a cancer are also provided. In
certain embodiments the methods comprise performing a method for
identifying the presence of a cancer and/or an increased risk of a
cancer in a mammal as described herein using a sample from the
subject or receiving the results of such a method performed on the
sample; and when the method alone, or in combination with other
indicator(s) from a differential diagnosis for a cancer indicates
the presence of neoplastic cells in said subject, treating the
subject, or causing the subject to be treated, to remove and/or to
inhibit the growth or proliferation of the neoplastic cells. In
certain embodiments treating said subject comprises surgically
removing the cells. In certain embodiments treating the subject
comprises performing radiotherapy or causing radiotherapy to be
performed on said subject to kill said neoplastic cells. In certain
embodiments treating the subject comprises administering or causing
to be administered to the subject an anti-cancer drug (e.g.,
matuzumab, erbitux, vectibix, nimotuzumab, matuzumab, panitumumab,
fluorouracil, capecitabine, 5-trifluoromethyl-2'-deoxyuridine,
methotrexate, raltitrexed, pemetrexed, cytosine arabinoside,
6-mercaptopurine, azathioprine, 6-thioguanine, pentostatin,
fludarabine, cladribine, floxuridine, cyclophosphamide, neosar,
ifosfamide, thiotepa, 1,3-bis(2-chloroethyl)-1-nitosourea,
1,-(2-chloroethyl)-3-cyclohexyl-Initrosourea, hexamethylmelamine,
busulfan, procarbazine, dacarbazine, chlorambucil, melphalan,
cisplatin, carboplatin, oxaliplatin, bendamustine, carmustine,
chloromethine, dacarbazine, fotemustine, lomustine, mannosulfan,
nedaplatin, nimustine, prednimustine, ranimustine, satraplatin,
semustine, streptozocin, temozolomide, treosulfan, triaziquone,
triethylene melamine, thiotepa, triplatin tetranitrate,
trofosfamide, uramustine, doxorubicin, daunorubicin, mitoxantrone,
etoposide, topotecan, teniposide, irinotecan, camptosar,
camptothecin, belotecan, rubitecan, vincristine, vinblastine,
vinorelbine, vindesine, paclitaxel, docetaxel, abraxane,
ixabepilone, larotaxel, ortataxel, tesetaxel, vinflunine, imatinib
mesylate, sunitinib malate, sorafenib tosylate, nilotinib
hydrochloride monohydrate/, tasigna, semaxanib, vandetanib,
vatalanib, retinoic acid, a retinoic acid derivative, and the
like).
Methods of monitoring a treatment of a subject for a cancer are
also provided. In various embodiments the methods comprise
performing a method for identifying the presence of a cancer and/or
an increased risk of a cancer in a mammal as described herein on a
sample from the subject or receiving the results of such a method
performed on the sample before or during the treatment; and;
performing the method again on a second sample from the subject or
receiving the results of such a method performed on the second
sample at a later time during or after the treatment; where a
reduced number or severity of aneuploidy (e.g., a reduced
aneuploidy frequency and/or a decrease or absence of certain
aneuploidies) in the second measurement (e.g., as compared to the
first measurement) is an indicator of a positive course of
treatment and the same or increased number or severity of
aneuploidy in the second measurement (e.g., as compared to the
first measurement) is an indicator of a negative course of
treatment and, when said indicator is negative, adjusting said
treatment regimen to a more aggressive treatment regimen and/or to
a palliative treatment regimen.
Although the examples herein concern humans and the language is
primarily directed to human concerns, the concepts described herein
are applicable to genomes from any plant or animal.
INCORPORATION BY REFERENCE
All patents, patent applications, and other publications, including
all sequences disclosed within these references, referred to herein
are expressly incorporated herein by reference, to the same extent
as if each individual publication, patent or patent application was
specifically and individually indicated to be incorporated by
reference. All documents cited are, in relevant part, incorporated
herein by reference in their entireties for the purposes indicated
by the context of their citation herein. However, the citation of
any document is not to be construed as an admission that it is
prior art with respect to the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a flowchart of a method 100 for determining the presence
or absence of a copy number variation in a test sample comprising a
mixture of nucleic acids.
FIG. 2 depicts workflows for preparing a sequencing library
according to Illumina's full-length protocol, the abbreviated
protocol (ABB), the 2-STEP and 1-STEP methods as described herein.
"P" represents a purification step; and "X" indicates that the
purification step and or the DNA repair are excluded.
FIG. 3 depicts a workflow of embodiments of the method for
preparing a sequencing library on a solid surface.
FIG. 4 illustrates a flowchart of an embodiment 400 of the method
for verifying the integrity of a sample that is subjected to a
multistep singleplex sequencing bioassay.
FIG. 5 illustrates a flowchart of an embodiment 500 of the method
for verifying the integrity of a plurality of samples that are
subjected to a multistep multiplex sequencing bioassay.
FIG. 6 is a flowchart of a method 600 for simultaneously
determining the presence or absence of aneuploidy and the fetal
fraction in a maternal test sample comprising a mixture of fetal
and maternal nucleic acids.
FIG. 7 is a flowchart of a method 700 for determining the fetal
fraction in a maternal test sample comprising a mixture of fetal
and maternal nucleic acids using massively parallel sequencing
methods or size separation of polymorphic nucleic acid
sequences.
FIG. 8 is a flowchart of a method 800 for simultaneously
determining the presence or absence of fetal aneuploidy and the
fetal fraction in a maternal plasma test sample enriched for
polymorphic nucleic acids.
FIG. 9 is a flowchart of a method 900 for simultaneously
determining the presence or absence of fetal aneuploidy and the
fetal fraction in a maternal purified cfDNA test sample that has
been enriched with polymorphic nucleic acids.
FIG. 10 is a flowchart of a method 1000 for simultaneously
determining the presence or absence of fetal aneuploidy and the
fetal fraction in a sequencing library constructed from fetal and
maternal nucleic acids derived from a maternal test sample and
enriched with polymorphic nucleic acids.
FIG. 11 is a flowchart outlining alternative embodiments of the
method for determining fetal fraction by massively parallel
sequencing shown in FIG. 7.
FIG. 12 is a bar diagram showing the identification of fetal and
maternal polymorphic sequences (SNPs) used to determine fetal
fraction in a test sample. The total number of sequence reads
(Y-axis) mapped to the SNP sequences identified by rs numbers
(X-axis), and the relative level of fetal nucleic acids (*) are
shown.
FIG. 13 is a block diagram depicting classification of fetal and
maternal zygosity states for a given genomic position.
FIG. 14 shows a comparison of the results using a mixture model and
the known fetal fraction and estimated fetal fraction.
FIG. 15 presents error estimates by sequenced base position over 30
lanes of Illumina GA2 data aligned to human genome HG18 using Eland
with default parameters.
FIG. 16 shows that using the machine error rate as a known
parameter reduces the upward bias by a point.
FIG. 17 shows that simulated data using the machine error rate as a
known parameter enhancing the case 1 and 2 error models greatly
reduces the upward bias to less than a point for fetal fraction
below 0.2.
FIG. 18A is a flow chart depicting a method of classifying a CNV by
comparing fetal fraction values calculated by two different
techniques.
FIGS. 18B and 18C are together a flow chart depicting another
method of classifying a CNV by comparing fetal fraction values
calculated by two different techniques.
FIG. 19 is a block diagram of a dispersed system for processing a
test sample and ultimately making a diagnosis.
FIG. 20 schematically illustrates how different operations in
processing test samples may be grouped to be handled by different
elements of a system.
FIGS. 21A and 21B shows electropherograms of a cfDNA sequencing
library prepared according to the abbreviated protocol described in
Example 2a (FIG. 21A), and the protocol described in Example 2b
(FIG. 21B).
FIGS. 22A-22C provide graphs showing the average (n=16) of the
percent of the total number of sequence tags that mapped to each
human chromosome (% ChrN; FIG. 22A) when the sequencing library was
prepared according to the abbreviated protocol (ABB; .diamond.) and
when the sequencing library was prepared according to the
repair-free 2-STEP method (INSOL; .quadrature.); and the percent
sequence tags as a function of the size of the chromosome (FIG.
22B). FIG. 22C shows the percent of the ratio of tags mapped when
libraries were prepared using the 2-STEP method to that obtained
when libraries were made using the abbreviated (ABB) method as a
function of the GC content of the chromosomes.
FIGS. 23A and 23B show bar diagrams providing mean and standard
deviation of the percent of tags mapped to chromosomes X (FIG. 23A;
% ChrX) and Y (FIG. 23B; % ChrY) obtained from sequencing 10
samples of cfDNA purified from plasma of 10 pregnant women. FIG.
23A shows that a greater number of tags mapped to the X chromosome
when using the repair-free method (2-STEP) relative to that
obtained using the abbreviated method (ABB). FIG. 23B shows that
the percent tags that mapped to the Y chromosome when using the
repair-free 2-STEP method was not different from that when using
the abbreviated method (ABB).
FIG. 24 shows the ratio of the number of non-excluded sites (NE
sites) on the reference genome (hg18) to the total number of tags
mapped to the non-excluded sites for each of 5 samples from which
cfDNA was prepared and used to construct a sequencing library
according to the abbreviated protocol (ABB) described in Example 2
(filled bars), the in solution repair-free protocol (2-STEP; empty
bars), and the solid surface repair-free protocol (1-STEP; gray
bars).
FIGS. 25 and 25B are graphs showing the average (n=5) of the
percent of the total number of sequence tags that mapped to each
human chromosome (% ChrN; FIG. 25A) when the sequencing library was
prepared on solid surface according to the abbreviated protocol
(ABB; .diamond.), when the sequencing library was prepared
according to the repair-free 2-STEP method (.quadrature.), and when
the library was prepared according to the repair-free 1-STEP method
(.DELTA.); and the percent sequence tags as a function of the size
of the chromosome (FIG. 25B). The regression coefficient for mapped
tags obtained from sequencing libraries prepared according to the
abbreviated protocol (ABB; .diamond.), and the solid surface
repair-free protocol (2-STEP; .quadrature.). FIG. 25C shows the
ratio of percent mapped sequence tags per chromosome obtained from
sequencing libraries prepared according to the repair-free 2-STEP
protocol and the tags per chromosome obtained sequencing libraries
prepared according to the abbreviated protocol (ABB) as a function
of the percent GC content of each chromosome (.diamond.), and the
ratio of percent mapped sequence tags per chromosome obtained from
sequencing libraries prepared according to the repair-free 1-STEP
protocol and the tags per chromosome obtained sequencing libraries
prepared according to the abbreviated protocol (ABB) as a function
of the percent GC content of each chromosome (.quadrature.).
FIGS. 26A and 26B show a comparison of means and standard
deviations of the percent of tags mapped to chromosomes X (FIG.
26A) and Y (FIG. 26B) obtained from sequencing 5 samples of cfDNA
purified from plasma of 5 pregnant women from the ABB, 2-STEP and
1-STEP methods. FIG. 26A shows that a greater number of tags mapped
to the X chromosome when using the repair-free methods (2-STEP and
1-STEP) relative to that obtained using the abbreviated method
(ABB). FIG. 26B shows that the percent tags that mapped to the Y
chromosome when using the repair-free 2-STEP and 1-STEP methods was
not different from that when using the abbreviated method.
FIGS. 27A and 27B show a correlation between the amount of purified
cfDNA used to prepare the sequencing libraries and the resulting
amount of library product was made for 61 clinical samples prepared
using the ABB method in solution (FIG. 27A), and 35 research
samples prepared using the repair-free Solid Surface (SS) 1-STEP
method (FIG. 27B).
FIG. 28 shows the correlation between the amount of cfDNA used to
make a library and the amount of library product obtained using the
2-STEP (.quadrature.), the ABB (.diamond.), and the 1-STEP
(.DELTA.) methods.
FIG. 29 shows the percent of indexed sequence reads that were
obtained when indexed libraries were prepared using the 1-STEP
(open bars) and the 2-STEP (filled bars) and sequenced as 6-plex
i.e. 6 indexed samples/flow cell lane.
FIGS. 30A and 30B are graphs showing the average (n=42) of the
percent of the total number of sequence tags that mapped to each
human chromosome (% ChrN; FIG. 30A) when indexed sequencing
libraries were prepared on solid surface according to the 1-STEP
method and sequenced as 6-plex; and the percent sequence tags
obtained as a function of the size of the chromosome (FIG.
30B).
FIG. 31 shows the percent sequence tags mapped to the Y chromosome
(ChrY) relative to the percent tags mapped to the X chromosome
(ChrX).
FIGS. 32A and 32B illustrate the distribution of the chromosome
dose for chromosome 21 determined from sequencing cfDNA extracted
from a set of 48 blood samples obtained from human subjects each
pregnant with a male or a female fetus. Chromosome 21 doses for
qualified i.e. normal for chromosome 21 (O), and trisomy 21 test
samples are shown (.DELTA.) for chromosomes 1-12 and X (FIG. 32A),
and for chromosomes 1-22 and X (FIG. 32B).
FIGS. 33A and 33B illustrate the distribution of the chromosome
dose for chromosome 18 determined from sequencing cfDNA extracted
from a set of 48 blood samples obtained from human subjects each
pregnant with a male or a female fetus. Chromosome 18 doses for
qualified i.e. normal for chromosome 18 (O), and trisomy 18
(.DELTA.) test samples are shown for chromosomes 1-12 and X (FIG.
33A), and for chromosomes 1-22 and X (FIG. 33B).
FIGS. 34A and 34B illustrate the distribution of the chromosome
dose for chromosome 13 determined from sequencing cfDNA extracted
from a set of 48 blood samples obtained from human subjects each
pregnant with a male or a female fetus. Chromosome 13 doses for
qualified i.e. normal for chromosome 13 (O), and trisomy 13
(.DELTA.) test samples are shown for chromosomes 1-12 and X (FIG.
34A), and for chromosomes 1-22 and X (FIG. 34B).
FIGS. 35A and 35B illustrate the distribution of the chromosome
doses for chromosome X determined from sequencing cfDNA extracted
from a set of 48 test blood samples obtained from human subjects
each pregnant with a male or a female fetus. Chromosome X doses for
males (46,XY; (O)), females (46,XX; (.DELTA.)); monosomy X (45,X;
(+)), and complex karyotypes (Cplx (X)) samples are shown for
chromosomes 1-12 and X (FIG. 35A), and for chromosomes 1-22 and X
(FIG. 35B).
FIGS. 36A and 36B illustrate the distribution of the chromosome
doses for chromosome Y determined from sequencing cfDNA extracted
from a set of 48 test blood samples obtained from human subjects
each pregnant with a male or a female fetus. Chromosome Y doses for
males (46,XY; (.DELTA.)), females (46,XX; (O)); monosomy X (45,X;
(+)), and complex karyotypes (Cplx (X)) samples are shown for
chromosomes 1-12 (FIG. 36A), and for chromosomes 1-22 (FIG.
36B).
FIG. 37 shows the coefficient of variation (CV) for chromosomes 21
(.box-solid.), 18 (.circle-solid.) and 13 (.tangle-solidup.) that
was determined from the doses shown in FIGS. 32A and 32B, 33A and
33B, and 34A and 34B, respectively.
FIG. 38 shows the coefficient of variation (CV) for chromosomes X
(.box-solid.) and Y (.circle-solid.) that was determined from the
doses shown in FIGS. 35A and 35B and 36A and 36B, respectively.
FIG. 39 shows the cumulative distribution of GC fraction by human
chromosome. The vertical axis represents the frequency of the
chromosome with GC content below the value shown on the horizontal
axis.
FIG. 40 from illustrates the sequence doses (Y-axis) for a segment
of chromosome 11 (81000082-103000103 bp) determined from sequencing
cfDNA extracted from a set of 7 qualified samples (O) obtained and
1 test sample (.diamond-solid.) from pregnant human subjects. A
sample from a subject carrying a fetus with a partial aneuploidy of
chromosome 11 (.diamond-solid.) was identified.
FIGS. 41A-41E illustrate the distribution of normalized chromosome
doses for chromosome 21 (41A), chromosome 18 (41B), chromosome 13
(41C), chromosome X (41D) and chromosome Y (41E) relative to the
standard deviation of the mean (Y-axis) for the corresponding
chromosomes in the unaffected samples.
FIG. 42 shows normalized chromosome values for chromosomes 21 (O),
18 (.DELTA.), and 13 (.quadrature.) determined in samples from
training set 1 using normalizing chromosomes as described in
Example 12.
FIG. 43 shows normalized chromosome values for chromosomes 21 (O),
18 (.DELTA.), and 13 (.quadrature.) determined in samples from test
set 1 using normalizing chromosomes as described in Example 12.
FIG. 44 shows normalized chromosome values for chromosomes 21 (O)
and 18 (.DELTA.) determined in samples from test set 1 using the
normalizing method of Chiu et al. (normalizes the number of
sequence tags identified for the chromosome of interest with the
number of sequence tags obtained for the remaining chromosomes in
the sample; see elsewhere herein Example 13).
FIG. 45 shows normalized chromosome values for chromosomes 21 (O),
18 (.DELTA.), and 13 (.quadrature.) determined in samples from
training set 1 using systematically determined normalizing
chromosomes (as described in Example 13).
FIG. 46 shows normalized chromosome values for chromosomes X
(X-axis) and Y (Y-axis). The arrows point to the 5 (FIG. 46A) and 3
(FIG. 46B) monosomy X samples that were identified in the training
and test sets, respectively, as described in Example 13.
FIG. 47 shows normalized chromosome values for chromosomes 21 (O),
18 (.DELTA.), and 13 (.quadrature.) determined in samples from test
set 1 using systematically determined normalizing chromosomes (as
described in Example 13).
FIG. 48 shows normalized chromosome values for chromosome 9 (O)
determined in samples from test set 1 using systematically
determined normalizing chromosomes (as described in Example
13).
FIG. 49 shows normalized chromosome values for chromosomes 1-22
determined in samples from test set 1 using systematically
determined normalizing chromosomes (as described in Example
13).
FIG. 50 shows a flow diagram of the design (A) and random sampling
plan (B) for the study described in Example 16.
FIGS. 51A-51F show flow diagrams for the analyses for chromosomes
21, 18, and 13 (FIGS. 51A-51C, respectively), and gender analyses
for female, male, and monosomy X (FIGS. 51D-51F, respectively).
Ovals contain results obtained from sequencing information from the
laboratory, rectangles contain karyotype results, and rectangles
with rounded corners show comparative results used to determine
test performance (sensitivity and specificity). The dashed lines in
FIGS. 51A and 51B denote the relationship between mosaic samples
for T21 (n=3) and T18 (n=1) that were censored from the analysis of
chromosome 21 and 18, respectively, but were correctly determined
as described in Example 16.
FIG. 52 shows normalized chromosome values (NCV) versus karyotype
classifications for chromosomes 21 (.circle-solid.), 18
(.box-solid.), and 13 (.tangle-solidup.) for the test samples of
the study described in Example 16. Circled samples denote
unclassified samples with trisomy karyotype.
FIG. 53 shows normalized chromosome values for chromosome X (NCV)
versus karyotype classifications for gender classifications of the
test samples of the study described in Example 16. Samples with
female karyotypes (.smallcircle.), samples with male karyotypes
(.circle-solid.), samples with 45,X (.quadrature.), and samples
with other karyotypes i.e. XXX, XXY, and XYY (.box-solid.) are
shown.
FIG. 54 shows a plot of normalized chromosome values for chromosome
Y versus normalized chromosome values for chromosome X for the test
samples of the clinical study described in Example 16. Euploid male
and female samples (.smallcircle.), XXX samples (.circle-solid.),
45,X samples (X), XYY samples (.box-solid.), and XXY samples
(.tangle-solidup.) are shown. The dashed lines show the threshold
values used for classifying samples as described in Example 16.
FIG. 55 schematically illustrates one embodiment of a CNV
determination method described herein.
FIG. 56 shows a plot from Example 17 of the percent "ff" determined
using doses of chromosome 21 (ff.sub.21) as a function of the
percent "ff" determined using doses of chromosome X (ff.sub.X) in a
synthetic maternal sample (1) comprising DNA from a child with
trisomy 21
FIG. 57 shows a plot from Example 17 of the percent "ff" determined
using doses of chromosome 7 (ff.sub.7) as a function of the percent
"ff" determined using doses of chromosome X (ff.sub.X) in a
synthetic maternal sample (2) comprising DNA from a euploid mother
and her child who carries a partial deletion in chromosome 7.
FIG. 58 shows a plot from Example 17 of the percent "ff" determined
using doses of chromosome 15 (ff.sub.15) as a function of the
percent "ff" determined using doses of chromosome X (ff.sub.X) in a
synthetic maternal sample (3) comprising DNA from a euploid mother
and her child who is 25% mosaic with a partial duplication of
chromosome 15.
FIG. 59 shows a plot from Example 17 of the percent "ff" determined
using doses of chromosome 22 (ff.sub.22) and the NCVs derived
therefrom in artificial sample (4) comprising 0% child DNA (i), and
10% DNA from an unaffected twin son known not to have a partial
chromosomal aneuploidy of chromosome 22 (ii), and 10% DNA from the
affected twin son known to have a partial chromosomal aneuploidy of
chromosome 22 (iii).
FIG. 60 shows a plot from Example 18 of the CNffx versus CNff21
determined in the samples comprising the fetal T21 trisomy.
FIG. 61 shows a plot from Example 18 of the CNffx versus CNff18
determined in the samples comprising the fetal T18 trisomy.
FIG. 62 shows a plot from Example 18 of the CNffx versus CNff13
determined in the samples comprising the fetal T13 trisomy.
FIG. 63 shows a plot from Example 19 of NCV values for chromosomes
1-22 and X in the test sample.
FIG. 64 shows the fetal fraction obtained in Example 18 for the
samples with female fetuses affected by T21.
FIG. 65 shows the family 2139 z.sub.21j 1 Mb bin results for Chr 21
with 0% (solid circles) and 10% (empty circles) mixtures of the
affected son's DNA mixed with the mother's DNA.
FIG. 66 shows the family 1313 z.sub.7j 1 Mb bin results for Chr 7
with 0% (solid circles) and 10% (empty circles) mixtures of the
affected son's DNA mixed with the mother's DNA.
FIG. 67 shows the family 2877 z.sub.ij 1 Mb bin results for Chr 11
and 15 with 0% (solid circles) and 10% (empty circles) mixtures of
the affected son's DNA mixed with the mother's DNA.
FIG. 68(a)-(b) show clinical sample C65104 z.sub.ij 1 Mb bin
results with a karyotype with duplication in chromosome 6. Expanded
regions show z.sub.6j 1 Mb bin and 100 kb bin results.
FIG. 69(a)-(b) show the clinical sample C61154 z.sub.ij 1 Mb bin
results across the genome for clinical sample with a karyotype with
a small deletion in chromosome 7 (circled). Another small deletion
is detected in chromosome 8 (red circle). Expanded regions show
z.sub.7j and z.sub.8j 100 kb bin data.
FIG. 70 shows the clinical sample C61731 z.sub.ij 1 Mb bin results
across the genome for clinical sample with a karyotype with a small
deletion in chromosome 8. Expanded region show z.sub.8j 1 Mb bin
data.
FIG. 71 shows the clinical sample C62228 z.sub.ij 1 Mb bin results
across the genome for clinical sample with a karyotype with a
deletion in chromosome 15. Expanded region show z.sub.15j 1 Mb bin
data.
FIG. 72 shows the clinical sample C61093 z.sub.ij 1 Mb bin results
across the genome with a karyotype 46, XY, add(10)(q26). Expanded
regions show z.sub.10j and z.sub.17j 1 Mb bin data.
FIG. 73 shows the clinical sample C61233 z.sub.ij 1 Mb bin results
across the genome with a karyotype 46,XX,add(X)(p22.1). Expanded
regions show z.sub.3j and z.sub.Xj 1 Mb bin data.61233.
DETAILED DESCRIPTION
The disclosed embodiments concern methods, apparatus, and systems
for determining copy number variations (CNV) of a sequence of
interest in a test sample that comprises a mixture of nucleic acids
that are known or are suspected to differ in the amount of one or
more sequence of interest. Sequences of interest include genomic
segment sequences ranging from, e.g., kilobases (kb) to megabases
(Mb) to entire chromosomes that are known or are suspected to be
associated with a genetic or a disease condition. Examples of
sequences of interest include chromosomes associated with
well-known aneuploidies e.g. trisomy 21, and segments of
chromosomes that are multiplied in diseases such as cancer e.g.
partial trisomy 8 in acute myeloid leukemia. CNV that can be
determined according to the present method include monosomies and
trisomies of any one or more of autosomes 1-22, and of sex
chromosomes X and Y e.g. 45,X, 47,XXX, 47,XXY and 47,XYY, other
chromosomal polysomies i.e. tetrasomy and pentasomies including but
not limited to XXXX, XXXXX, XXXXY and XYYYY, and deletions and/or
duplications of segments of any one or more of the chromosomes.
The methods employ a statistical approach that is implemented on
machine processor(s) and accounts for accrued variability stemming
from, e.g., process-related, interchromosomal (intra-run), and
inter-sequencing (inter-run) variability. The methods are
applicable to determining CNV of any fetal aneuploidy, and CNVs
known or suspected to be associated with a variety of medical
conditions.
Unless otherwise indicated, the practice of the present invention
involves conventional techniques and apparatus commonly used in
molecular biology, microbiology, protein purification, protein
engineering, protein and DNA sequencing, and recombinant DNA
fields, which are within the skill of the art. Such techniques and
apparatus are known to those of skill in the art and are described
in numerous texts and reference works (See e.g., Sambrook et al.,
"Molecular Cloning: A Laboratory Manual", Third Edition (Cold
Spring Harbor), [2001]); and Ausubel et al., "Current Protocols in
Molecular Biology" [1987]).
Numeric ranges are inclusive of the numbers defining the range. It
is intended that every maximum numerical limitation given
throughout this specification includes every lower numerical
limitation, as if such lower numerical limitations were expressly
written herein. Every minimum numerical limitation given throughout
this specification will include every higher numerical limitation,
as if such higher numerical limitations were expressly written
herein.
Every numerical range given throughout this specification will
include every narrower numerical range that falls within such
broader numerical range, as if such narrower numerical ranges were
all expressly written herein.
The headings provided herein are not intended to limit the
disclosure.
Unless defined otherwise herein, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art. Various scientific dictionaries that
include the terms included herein are well known and available to
those in the art. Although any methods and materials similar or
equivalent to those described herein find use in the practice or
testing of the embodiments disclosed herein, some methods and
materials are described.
The terms defined immediately below are more fully described by
reference to the Specification as a whole. It is to be understood
that this disclosure is not limited to the particular methodology,
protocols, and reagents described, as these may vary, depending
upon the context they are used by those of skill in the art.
DEFINITIONS
As used herein, the singular terms "a", "an," and "the" include the
plural reference unless the context clearly indicates otherwise.
Unless otherwise indicated, nucleic acids are written left to right
in 5' to 3' orientation and amino acid sequences are written left
to right in amino to carboxy orientation, respectively.
The term "assessing" when used herein in the context of analyzing a
nucleic acid sample for CNV refers to characterizing the status of
a chromosomal or segment aneuploidy by one of three types of calls:
"normal" or "unaffected", "affected", and "no-call". Thresholds for
calling normal and affected are typically set. A parameter related
to aneuploidy is measured in a sample and the measured value is
compared to the thresholds. For duplication type aneuploidies, a
call of affected is made if a chromosome or segment dose (or other
measured value sequence content) is above a defined threshold set
for affected samples. For such aneuploidies, a call of normal is
made if the chromosome or segment dose is below a threshold set for
normal samples. By contrast for deletion type aneuploidies, a call
of affected is made if a chromosome or segment dose is below a
defined threshold for affected samples, and a call of normal is
made if the chromosome or segment dose is above a threshold set for
normal samples. For example, in the presence of trisomy the
"normal" call is determined by the value of a parameter e.g. a test
chromosome dose that is below a user-defined threshold of
reliability, and the "affected" call is determined by a parameter
e.g. a test chromosome dose, that is above a user-defined threshold
of reliability. A "no-call" result is determined by a parameter,
e.g. a test chromosome dose, that lies between the thresholds for
making a "normal" or an "affected" call. The term "no-call" is used
interchangeably with "unclassified".
The term "copy number variation" herein refers to variation in the
number of copies of a nucleic acid sequence present in a test
sample in comparison with the copy number of the nucleic acid
sequence present in a qualified sample. In certain embodiments, the
nucleic acid sequence is 1 kb or larger. In some cases, the nucleic
acid sequence is a whole chromosome or significant portion thereof.
A "copy number variant" refers to the sequence of nucleic acid in
which copy-number differences are found by comparison of a sequence
of interest in test sample with an expected level of the sequence
of interest. For example, the level of the sequence of interest in
the test sample is compared to that present in a qualified sample.
Copy number variants/variations include deletions, including
microdeletions, insertions, including microinsertions,
duplications, multiplications, inversions, translocations and
complex multi-site variants. CNVs encompass chromosomal
aneuploidies and partial aneuploidies.
The term "aneuploidy" herein refers to an imbalance of genetic
material caused by a loss or gain of a whole chromosome, or part of
a chromosome.
The terms "chromosomal aneuploidy" and "complete chromosomal
aneuploidy" herein refer to an imbalance of genetic material caused
by a loss or gain of a whole chromosome, and includes germline
aneuploidy and mosaic aneuploidy.
The terms "partial aneuploidy" and "partial chromosomal aneuploidy"
herein refer to an imbalance of genetic material caused by a loss
or gain of part of a chromosome e.g. partial monosomy and partial
trisomy, and encompasses imbalances resulting from translocations,
deletions and insertions.
The term "aneuploid sample" herein refers to a sample indicative of
a subject whose chromosomal content is not euploid, i.e. the sample
is indicative of a subject with an abnormal copy number of
chromosomes or portions or chromosomes.
The term "aneuploid chromosome" herein refers to a chromosome that
is known or determined to be present in a sample in an abnormal
copy number.
The term "plurality" refers to more than one element. For example,
the term is used herein in reference to a number of nucleic acid
molecules or sequence tags that is sufficient to identify
significant differences in copy number variations (e.g. chromosome
doses) in test samples and qualified samples using the methods
disclosed herein. In some embodiments, at least about 3.times.106
sequence tags, at least about 5.times.106 sequence tags, at least
about 8.times.106 sequence tags, at least about 10.times.106
sequence tags, at least about 15.times.106 sequence tags, at least
about 20.times.106 sequence tags, at least about 30.times.106
sequence tags, at least about 40.times.106 sequence tags, or at
least about 50.times.106 sequence tags comprising between about 20
and 40 bp reads are obtained for each test sample.
The terms "polynucleotide", "nucleic acid" and "nucleic acid
molecules" are used interchangeably and refer to a covalently
linked sequence of nucleotides (i.e., ribonucleotides for RNA and
deoxyribonucleotides for DNA) in which the 3' position of the
pentose of one nucleotide is joined by a phosphodiester group to
the 5' position of the pentose of the next, include sequences of
any form of nucleic acid, including, but not limited to RNA and DNA
molecules such as cfDNA molecules. The term "polynucleotide"
includes, without limitation, single- and double-stranded
polynucleotide.
The term "portion" is used herein in reference to the amount of
sequence information of fetal and maternal nucleic acid molecules
in a biological sample that in sum amount to less than the sequence
information of 1 human genome.
The term "test sample" herein refers to a sample, typically derived
from a biological fluid, cell, tissue, organ, or organism,
comprising a nucleic acid or a mixture of nucleic acids comprising
at least one nucleic acid sequence that is to be screened for copy
number variation. In certain embodiments the sample comprises at
least one nucleic acid sequence whose copy number is suspected of
having undergone variation. Such samples include, but are not
limited to sputum/oral fluid, amniotic fluid, blood, a blood
fraction, or fine needle biopsy samples (e.g., surgical biopsy,
fine needle biopsy, etc.) urine, peritoneal fluid, pleural fluid,
and the like. Although the sample is often taken from a human
subject (e.g., patient), the assays can be used to copy number
variations (CNVs) in samples from any mammal, including, but not
limited to dogs, cats, horses, goats, sheep, cattle, pigs, etc. The
sample may be used directly as obtained from the biological source
or following a pretreatment to modify the character of the sample.
For example, such pretreatment may include preparing plasma from
blood, diluting viscous fluids and so forth. Methods of
pretreatment may also involve, but are not limited to, filtration,
precipitation, dilution, distillation, mixing, centrifugation,
freezing, lyophilization, concentration, amplification, nucleic
acid fragmentation, inactivation of interfering components, the
addition of reagents, lysing, etc. If such methods of pretreatment
are employed with respect to the sample, such pretreatment methods
are typically such that the nucleic acid(s) of interest remain in
the test sample, preferably at a concentration proportional to that
in an untreated test sample (e.g., namely, a sample that is not
subjected to any such pretreatment method(s)). Such "treated" or
"processed" samples are still considered to be biological "test"
samples with respect to the methods described herein.
The term "qualified sample" herein refers to a sample comprising a
mixture of nucleic acids that are present in a known copy number to
which the nucleic acids in a test sample are to be compared, and it
is a sample that is normal i.e. not aneuploid, for the sequence of
interest. In certain embodiments, qualified samples are used for
identifying one or more normalizing chromosomes or segments for a
chromosome under consideration. For example, qualified samples may
be used for identifying a normalizing chromosome for chromosome 21.
In such case, the qualified sample is a sample that is not a
trisomy 21 sample. Qualified samples may also be employed in
determining thresholds for calling affected samples.
The term "training set" herein refers to a set of samples that can
comprise affected and unaffected samples and are used to develop a
model for analyzing test samples. The unaffected samples in a
training set may be used as the qualified samples to identify
normalizing sequences, e.g., normalizing chromosomes, and the
chromosome doses of unaffected samples are used to set the
thresholds for each of the sequences, e.g. chromosomes, of
interest. The affected samples in a training set can be used to
verify that affected test samples can be easily differentiated from
unaffected samples.
The term "qualified nucleic acid" is used interchangeably with
"qualified sequence" is a sequence against which the amount of a
test sequence or test nucleic acid is compared. A qualified
sequence is one present in a biological sample preferably at a
known representation i.e. the amount of a qualified sequence is
known. Generally, a qualified sequence is the sequence present in a
"qualified sample". A "qualified sequence of interest" is a
qualified sequence for which the amount is known in a qualified
sample, and is a sequence that is associated with a difference in
sequence representation in an individual with a medical
condition.
The term "sequence of interest" herein refers to a nucleic acid
sequence that is associated with a difference in sequence
representation in healthy versus diseased individuals. A sequence
of interest can be a sequence on a chromosome that is
misrepresented i.e. over- or under-represented, in a disease or
genetic condition. A sequence of interest may be a portion of a
chromosome i.e. chromosome segment, or a chromosome. For example, a
sequence of interest can be a chromosome that is over-represented
in an aneuploidy condition, or a gene encoding a tumor-suppressor
that is under-represented in a cancer. Sequences of interest
include sequences that are over- or under-represented in the total
population, or a subpopulation of cells of a subject. A "qualified
sequence of interest" is a sequence of interest in a qualified
sample. A "test sequence of interest" is a sequence of interest in
a test sample.
The term "normalizing sequence" herein refers to a sequence that is
used to normalize the number of sequence tags mapped to a sequence
of interest associated with the normalizing sequence. In some
embodiments, the normalizing sequence displays a variability in the
number of sequence tags that are mapped to it among samples and
sequencing runs that approximates the variability of the sequence
of interest for which it is used as a normalizing parameter, and
that can differentiate an affected sample from one or more
unaffected samples. In some implementations, the normalizing
sequence best or effectively differentiates, when compared to other
potential normalizing sequences such as other chromosomes, an
affected sample from one or more unaffected samples. A "normalizing
chromosome" or "normalizing chromosome sequence" is an example of a
"normalizing sequence". A "normalizing chromosome sequence" can be
composed of a single chromosome or of a group of chromosomes. A
"normalizing segment" is another example of a "normalizing
sequence". A "normalizing segment sequence" can be composed of a
single segment of a chromosome or it can be composed of two or more
segments of the same or of different chromosomes. In certain
embodiments, a normalizing sequence is intended to normalize for
variability such as process-related, interchromosomal (intra-run),
and inter-sequencing (inter-run) variability.
The term "differentiability" herein refers to the characteristic of
a normalizing chromosome that enables to distinguish one or more
unaffected i.e. normal, samples from one or more affected i.e.
aneuploid, samples.
The term "sequence dose" herein refers to a parameter that relates
the number of sequence tags identified for a sequence of interest
and the number of sequence tags identified for the normalizing
sequence. In some cases, the sequence dose is the ratio of the
number of sequence tags identified for a sequence of interest to
the number of sequence tags identified for the normalizing
sequence. In some cases, the sequence dose refers to a parameter
that relates the sequence tag density of a sequence of interest to
the tag density of a normalizing sequence. A "test sequence dose"
is a parameter that relates the sequence tag density of a sequence
of interest, e.g. chromosome 21, to that of a normalizing sequence
e.g. chromosome 9, determined in a test sample. Similarly, a
"qualified sequence dose" is a parameter that relates the sequence
tag density of a sequence of interest to that of a normalizing
sequence determined in a qualified sample.
The term "sequence tag density" herein refers to the number of
sequence reads that are mapped to a reference genome sequence; e.g.
the sequence tag density for chromosome 21 is the number of
sequence reads generated by the sequencing method that are mapped
to chromosome 21 of the reference genome. The term "sequence tag
density ratio" herein refers to the ratio of the number of sequence
tags that are mapped to a chromosome of the reference genome e.g.
chromosome 21, to the length of the reference genome
chromosome.
The term "Next Generation Sequencing (NGS)" herein refers to
sequencing methods that allow for massively parallel sequencing of
clonally amplified molecules and of single nucleic acid molecules.
Non-limiting examples of NGS include sequencing-by-synthesis using
reversible dye terminators, and sequencing-by-ligation.
The term "parameter" herein refers to a numerical value that
characterizes a physical property. Frequently, a parameter
numerically characterizes a quantitative data set and/or a
numerical relationship between quantitative data sets. For example,
a ratio (or function of a ratio) between the number of sequence
tags mapped to a chromosome and the length of the chromosome to
which the tags are mapped, is a parameter.
The terms "threshold value" and "qualified threshold value" herein
refer to any number that is used as a cutoff to characterize a
sample such as a test sample containing a nucleic acid from an
organism suspected of having a medical condition. The threshold may
be compared to a parameter value to determine whether a sample
giving rise to such parameter value suggests that the organism has
the medical condition. In certain embodiments, a qualified
threshold value is calculated using a qualifying data set and
serves as a limit of diagnosis of a copy number variation e.g. an
aneuploidy, in an organism. If a threshold is exceeded by results
obtained from methods disclosed herein, a subject can be diagnosed
with a copy number variation e.g. trisomy 21. Appropriate threshold
values for the methods described herein can be identified by
analyzing normalizing values (e.g. chromosome doses, NCVs or NSVs)
calculated for a training set of samples. Threshold values can be
identified using qualified (i.e. unaffected) samples in a training
set which comprises both qualified (i.e. unaffected) samples and
affected samples. The samples in the training set known to have
chromosomal aneuploidies (i.e. the affected samples) can be used to
confirm that the chosen thresholds are useful in differentiating
affected from unaffected samples in a test set (see the Examples
herein). The choice of a threshold is dependent on the level of
confidence that the user wishes to have to make the classification.
In some embodiments, the training set used to identify appropriate
threshold values comprises at least 10, at least 20, at least 30,
at least 40, at least 50, at least 60, at least 70, at least 80, at
least 90, at least 100, at least 200, at least 300, at least 400,
at least 500, at least 600, at least 700, at least 800, at least
900, at least 1000, at least 2000, at least 3000, at least 4000, or
more qualified samples. It may advantageous to use larger sets of
qualified samples to improve the diagnostic utility of the
threshold values.
The term "normalizing value" herein refers to a numerical value
that relates the number of sequence tags identified for the
sequence (e.g. chromosome or chromosome segment) of interest to the
number of sequence tags identified for the normalizing sequence
(e.g. normalizing chromosome or normalizing chromosome segment).
For example, a "normalizing value" can be a chromosome dose as
described elsewhere herein, or it can be an NCV (Normalized
Chromosome Value) as described elsewhere herein, or it can be an
NSV (Normalized Segment Value) as described elsewhere herein.
The term "read" refers to a sequence read from a portion of a
nucleic acid sample. Typically, though not necessarily, a read
represents a short sequence of contiguous base pairs in the sample.
The read may be represented symbolically by the base pair sequence
(in ATCG) of the sample portion. It may be stored in a memory
device and processed as appropriate to determine whether it matches
a reference sequence or meets other criteria. A read may be
obtained directly from a sequencing apparatus or indirectly from
stored sequence information concerning the sample. In some cases, a
read is a. DNA sequence of sufficient length (e.g., at least about
30 bp) that can be used to identify a larger sequence or region,
e.g. that can be aligned and specifically assigned to a chromosome
or genomic region or gene.
The term "sequence tag" is herein used interchangeably with the
term "mapped sequence tag" to refer to a sequence read that has
been specifically assigned i.e. mapped, to a larger sequence e.g. a
reference genome, by alignment. Mapped sequence tags are uniquely
mapped to a reference genome i.e. they are assigned to a single
location to the reference genome. Tags may be provided as data
structures or other assemblages of data. In certain embodiments, a
tag contains a read sequence and associated information for that
read such as the location of the sequence in the genome, e.g., the
position on a chromosome. In certain embodiments, the location is
specified for a positive strand orientation. A tag may be defined
to provide a limit amount of mismatch in aligning to a reference
genome. Tags that can be mapped to more than one location on a
reference genome i.e. tags that do not map uniquely, may not be
included in the analysis.
As used herein, the terms "aligned", "alignment", or "aligning"
refer to the process of comparing a read or tag to a reference
sequence and thereby determining whether the reference sequence
contains the read sequence. If the reference sequence contains the
read, the read may be mapped to the reference sequence or, in
certain embodiments, to a particular location in the reference
sequence. In some cases, alignment simply tells whether or not a
read is a member of a particular reference sequence (i.e., whether
the read is present or absent in the reference sequence). For
example, the alignment of a read to the reference sequence for
human chromosome 13 will tell whether the read is present in the
reference sequence for chromosome 13. A tool that provides this
information may be called a set membership tester. In some cases,
an alignment additionally indicates a location in the reference
sequence where the read or tag maps to. For example, if the
reference sequence is the whole human genome sequence, an alignment
may indicate that a read is present on chromosome 13, and may
further indicate that the read is on a particular strand and/or
site of chromosome 13.
Aligned reads or tags are one or more sequences that are identified
as a match in terms of the order of their nucleic acid molecules to
a known sequence from a reference genome. Alignment can be done
manually, although it is typically implemented by a computer
algorithm, as it would be impossible to align reads in a reasonable
time period for implementing the methods disclosed herein. One
example of an algorithm from aligning sequences is the Efficient
Local Alignment of Nucleotide Data (ELAND) computer program
distributed as part of the Illumina Genomics Analysis pipeline.
Alternatively, a Bloom filter or similar set membership tester may
be employed to align reads to reference genomes. See U.S. Patent
Application No. 61/552,374 filed Oct. 27, 2011 which is
incorporated herein by reference in its entirety. The matching of a
sequence read in aligning can be a 100% sequence match or less than
100% (non-perfect match).
As used herein, the term "reference genome" or "reference sequence"
refers to any particular known genome sequence, whether partial or
complete, of any organism or virus which may be used to reference
identified sequences from a subject. For example, a reference
genome used for human subjects as well as many other organisms is
found at the National Center for Biotechnology Information at
www.ncbi.nlm.nih.gov. A "genome" refers to the complete genetic
information of an organism or virus, expressed in nucleic acid
sequences.
In various embodiments, the reference sequence is significantly
larger than the reads that are aligned to it. For example, it may
be at least about 100 times larger, or at least about 1000 times
larger, or at least about 10,000 times larger, or at least about
10.sup.5 times larger, or at least about 10.sup.6 times larger, or
at least about 10.sup.7 times larger.
In one example, the reference sequence is that of a full length
human genome. Such sequences may be referred to as genomic
reference sequences. In another example, the reference sequence is
limited to a specific human chromosome such as chromosome 13. Such
sequences may be referred to as chromosome reference sequences.
Other examples of reference sequences include genomes of other
species, as well as chromosomes, sub-chromosomal regions (such as
strands), etc. of any species.
In various embodiments, the reference sequence is a consensus
sequence or other combination derived from multiple individuals.
However, in certain applications, the reference sequence may be
taken from a particular individual.
The term "artificial target sequences genome" herein refers to a
grouping of known sequences that encompass alleles of known
polymorphic sites. For example, a "SNP reference genome" is an
artificial target sequences genome comprising a grouping of
sequences that encompass alleles of known SNPs.
The term "clinically-relevant sequence" herein refers to a nucleic
acid sequence that is known or is suspected to be associated or
implicated with a genetic or disease condition. Determining the
absence or presence of a clinically-relevant sequence can be useful
in determining a diagnosis or confirming a diagnosis of a medical
condition, or providing a prognosis for the development of a
disease.
The term "derived" when used in the context of a nucleic acid or a
mixture of nucleic acids, herein refers to the means whereby the
nucleic acid(s) are obtained from the source from which they
originate. For example, in one embodiment, a mixture of nucleic
acids that is derived from two different genomes means that the
nucleic acids e.g. cfDNA, were naturally released by cells through
naturally occurring processes such as necrosis or apoptosis. In
another embodiment, a mixture of nucleic acids that is derived from
two different genomes means that the nucleic acids were extracted
from two different types of cells from a subject.
The term "patient sample" herein refers to a biological sample
obtained from a patient i.e. a recipient of medical attention, care
or treatment. The patient sample can be any of the samples
described herein. In certain embodiments, the patient sample is
obtained by non-invasive procedures e.g. peripheral blood sample or
a stool sample. The methods described herein need not be limited to
humans. Thus, various veterinary applications are contemplated in
which case the patient sample may be a sample from a non-human
mammal (e.g., a feline, a porcine, an equine, a bovine, and the
like).
The term "mixed sample" herein refers to a sample containing a
mixture of nucleic acids, which are derived from different
genomes.
The term "maternal sample" herein refers to a biological sample
obtained from a pregnant subject e.g. a woman.
The term "biological fluid" herein refers to a liquid taken from a
biological source and includes, for example, blood, serum, plasma,
sputum, lavage fluid, cerebrospinal fluid, urine, semen, sweat,
tears, saliva, and the like. As used herein, the terms "blood,"
"plasma" and "serum" expressly encompass fractions or processed
portions thereof. Similarly, where a sample is taken from a biopsy,
swab, smear, etc., the "sample" expressly encompasses a processed
fraction or portion derived from the biopsy, swab, smear, etc.
The terms "maternal nucleic acids" and "fetal nucleic acids" herein
refer to the nucleic acids of a pregnant female subject and the
nucleic acids of the fetus being carried by the pregnant female,
respectively.
As used herein, the term "corresponding to" sometimes refers to a
nucleic acid sequence e.g. a gene or a chromosome, that is present
in the genome of different subjects, and which does not necessarily
have the same sequence in all genomes, but serves to provide the
identity rather than the genetic information of a sequence of
interest e.g. a gene or chromosome.
As used herein, the term "substantially cell free" encompasses
preparations of the desired sample from which cell components that
are normally associated with it are removed. For example, a plasma
sample is rendered substantially cell free by removing blood cells
e.g. red cells, which are normally associated with it. In some
embodiments, substantially free samples are processed to remove
cells that would otherwise contribute to the desired genetic
material that is to be tested for a CNV.
As used herein, the term "fetal fraction" refers to the fraction of
fetal nucleic acids present in a sample comprising fetal and
maternal nucleic acid. Fetal fraction is often used to characterize
the cfDNA in a mother's blood.
As used herein the term "chromosome" refers to the heredity-bearing
gene carrier of a living cell which is derived from chromatin and
which comprises DNA and protein components (especially histones).
The conventional internationally recognized individual human genome
chromosome numbering system is employed herein.
As used herein, the term "polynucleotide length" refers to the
absolute number of nucleic acid molecules (nucleotides) in a
sequence or in a region of a reference genome. The term "chromosome
length" refers to the known length of the chromosome given in base
pairs e.g. provided in the NCBI36/hg18 assembly of the human
chromosome found on the world wide web at
genome.ucsc.edu/cgi-bin/hgTracks?hgsid=167155613&chromInfoPage=
The term "subject" herein refers to a human subject as well as a
non-human subject such as a mammal, an invertebrate, a vertebrate,
a fungus, a yeast, a bacteria, and a virus. Although the examples
herein concern humans and the language is primarily directed to
human concerns, the concepts disclosed herein are applicable to
genomes from any plant or animal, and are useful in the fields of
veterinary medicine, animal sciences, research laboratories and
such.
The term "condition" herein refers to "medical condition" as a
broad term that includes all diseases and disorders, but can
include [injuries] and normal health situations, such as pregnancy,
that might affect a person's health, benefit from medical
assistance, or have implications for medical treatments.
The term "complete" is used herein in reference to a chromosomal
aneuploidy to refer to a gain or loss of an entire chromosome.
The term "partial" when used in reference to a chromosomal
aneuploidy herein refers to a gain or loss of a portion i.e.
segment, of a chromosome.
The term "mosaic" herein refers to denote the presence of two
populations of cells with different karyotypes in one individual
who has developed from a single fertilized egg. Mosaicism may
result from a mutation during development which is propagated to
only a subset of the adult cells.
The term "non-mosaic" herein refers to an organism e.g. a human
fetus, composed of cells of one karyotype.
The term "using a chromosome" when used in reference to determining
a chromosome dose, herein refers to using the sequence information
obtained for a chromosome i.e. the number of sequence tags obtained
for a chromosome.
The term "sensitivity" as used herein is equal to the number of
true positives divided by the sum of true positives and false
negatives.
The term "specificity" as used herein is equal to the number of
true negatives divided by the sum of true negatives and false
positives.
The term "hypodiploid" herein refers to a chromosome number that is
one or more lower than the normal haploid number of chromosomes
characteristic for the species.
A "polymorphic site" is a locus at which nucleotide sequence
divergence occurs. The locus may be as small as one base pair.
Illustrative markers have at least two alleles, each occurring at
frequency of greater than 1%, and more typically greater than 10%
or 20% of a selected population. A polymorphic site may be the site
of a single nucleotide polymorphism (SNP), a small-scale multi-base
deletion or insertion, a Multi-Nucleotide Polymorphism (MNP) or a
Short Tandem Repeat (STR). The terms "polymorphic locus" and
"polymorphic site" are herein used interchangeably.
A "polymorphic sequence" herein refers to a nucleic acid sequence
e.g. a DNA sequence, that comprises one or more polymorphic sites
e.g one SNP or a tandem SNP. Polymorphic sequences according to the
present technology can be used to specifically differentiate
between maternal and non-maternal alleles in the maternal sample
comprising a mixture of fetal and maternal nucleic acids.
A "single nucleotide polymorphism" (SNP) as used herein occurs at a
polymorphic site occupied by a single nucleotide, which is the site
of variation between allelic sequences. The site is usually
preceded by and followed by highly conserved sequences of the
allele (e.g., sequences that vary in less than 1/100 or 1/1000
members of the populations). A SNP usually arises due to
substitution of one nucleotide for another at the polymorphic site.
A transition is the replacement of one purine by another purine or
one pyrimidine by another pyrimidine. A transversion is the
replacement of a purine by a pyrimidine or vice versa. SNPs can
also arise from a deletion of a nucleotide or an insertion of a
nucleotide relative to a reference allele. Single nucleotide
polymorphisms (SNPs) are positions at which two alternative bases
occur at appreciable frequency (>1%) in the human population,
and are the most common type of human genetic variation.
The term "tandem SNPs" herein refers to two or more SNPs that are
present within a polymorphic target nucleic acid sequence.
The term "short tandem repeat" or "STR" as used herein refers to a
class of polymorphisms that occurs when a pattern of two or more
nucleotides are repeated and the repeated sequences are directly
adjacent to each other. The pattern can range in length from 2 to
10 base pairs (bp) (for example (CATG).sub.n in a genomic region)
and is typically in the non-coding intron region. By examining
several STR loci and counting how many repeats of a specific STR
sequence there are at a given locus, it is possible to create a
unique genetic profile of an individual.
As used herein, the term "miniSTR" herein refers to tandem repeat
of four or more base pairs that spans less than about 300 base
pairs, less than about 250 base airs, less than about 200 base
pairs, less than about 150 base pairs, less than about 100 base
pairs, less than about 50 base pairs, or less than about 25 base
pairs. "miniSTRs" are STRs that are amplifiable from cfDNA
templates.
The terms "polymorphic target nucleic acid," "polymorphic
sequence," "polymorphic target nucleic acid sequence" and
"polymorphic nucleic acid" are used interchangeably herein to refer
to a nucleic acid sequence (e.g. a DNA sequence) that comprises one
or more polymorphic sites.
The term "plurality of polymorphic target nucleic acids" herein
refers to a number of nucleic acid sequences each comprising at
least one polymorphic site, e.g. one SNP, such that at least 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40 or more different
polymorphic sites are amplified from the polymorphic target nucleic
acids to identify and/or quantify fetal alleles present in maternal
samples comprising fetal and maternal nucleic acids.
The term "enrich" herein refers to the process of amplifying
polymorphic target nucleic acids contained in a portion of a
maternal sample, and combining the amplified product with the
remainder of the maternal sample from which the portion was
removed. For example, the remainder of the maternal sample can be
the original maternal sample.
The term "original maternal sample" herein refers to a non-enriched
biological sample obtained from a pregnant subject e.g. a woman,
who serves as the source from which a portion is removed to amplify
polymorphic target nucleic acids. The "original sample" can be any
sample obtained from a pregnant subject, and the processed
fractions thereof e.g. a purified cfDNA sample extracted from a
maternal plasma sample.
The term "primer," as used herein refers to an isolated
oligonucleotide which is capable of acting as a point of initiation
of synthesis when placed under conditions in which synthesis of a
primer extension product, which is complementary to a nucleic acid
strand, is induced (i.e., in the presence of nucleotides and an
inducing agent such as DNA polymerase and at a suitable temperature
and pH). The primer is preferably single stranded for maximum
efficiency in amplification, but may alternatively be double
stranded. If double stranded, the primer is first treated to
separate its strands before being used to prepare extension
products. Preferably, the primer is an oligodeoxyribonucleotide.
The primer must be sufficiently long to prime the synthesis of
extension products in the presence of the inducing agent. The exact
lengths of the primers will depend on many factors, including
temperature, source of primer, use of the method, and the
parameters used for primer design.
The phrase "cause to be administered" refers to the actions taken
by a medical professional (e.g., a physician), or a person
controlling or directing medical care of a subject, that control
and/or permit the administration of the agent(s)/compound(s) at
issue to the subject. Causing to be administered can involve
diagnosis and/or determination of an appropriate therapeutic or
prophylactic regimen, and/or prescribing particular
agent(s)/compounds for a subject. Such prescribing can include, for
example, drafting a prescription form, annotating a medical record,
and the like. Similarly, "cause to be performed", e.g., for a
diagnostic procedure refers to the actions taken by a medical
professional (e.g., a physician), or a person controlling or
directing medical care of a subject, that control and/or permit the
performance of one or more diagnostic protocols to or on the
subject.
Introduction
Disclosed herein are methods, apparatus, and systems for
determining copy number variations (CNV) of different sequences of
interest in a test sample that comprises a mixture of nucleic acids
derived from two different genomes, and which are known or are
suspected to differ in the amount of one or more sequence of
interest. Copy number variations determined by the methods and
apparatus disclosed hereininclude gains or losses of entire
chromosomes, alterations involving very large chromosomal segments
that are microscopically visible, and an abundance of
sub-microscopic copy number variation of DNA segments ranging from
kilobases (kb) to megabases (Mb) in size. In various embodiments,
the methods comprise a machine-implemented statistical approach
that accounts for accrued variability stemming from
process-related, interchromosomal and inter-sequencing variability.
The method is applicable to determining CNV of any fetal
aneuploidy, and CNVs known or suspected to be associated with a
variety of medical conditions. CNV that can be determined according
to the present method include trisomies and monosomies of any one
or more of chromosomes 1-22, X and Y, other chromosomal polysomies,
and deletions and/or duplications of segments of any one or more of
the chromosomes, which can be detected by sequencing only once the
nucleic acids of a test sample. Any aneuploidy can be determined
from sequencing information that is obtained by sequencing only
once the nucleic acids of a test sample.
CNV in the human genome significantly influence human diversity and
predisposition to disease (Redon et al., Nature 23:444-454 [2006],
Shaikh et al. Genome Res 19:1682-1690 [2009]). CNVs have been known
to contribute to genetic disease through different mechanisms,
resulting in either imbalance of gene dosage or gene disruption in
most cases. In addition to their direct correlation with genetic
disorders, CNVs are known to mediate phenotypic changes that can be
deleterious. Recently, several studies have reported an increased
burden of rare or de novo CNVs in complex disorders such as Autism,
ADHD, and schizophrenia as compared to normal controls,
highlighting the potential pathogenicity of rare or unique CNVs
(Sebat et al., 316:445-449 [2007]; Walsh et al., Science
320:539-543 [2008]). CNV arise from genomic rearrangements,
primarily owing to deletion, duplication, insertion, and unbalanced
translocation events.
The methods and apparatus described herein may employ next
generation sequencing technology (NGS), which is massively parallel
sequencing. In certain embodiments, clonally amplified DNA
templates or single DNA molecules are sequenced in a massively
parallel fashion within a flow cell (e.g. as described in
Volkerding et al. Clin Chem 55:641-658 [2009]; Metzker M Nature Rev
11:31-46 [2010]). In addition to high-throughput sequence
information, NGS provides quantitative information, in that each
sequence read is a countable "sequence tag" representing an
individual clonal DNA template or a single DNA molecule. The
sequencing technologies of NGS include pyrosequencing,
sequencing-by-synthesis with reversible dye terminators, sequencing
by oligonucleotide probe ligation and ion semiconductor sequencing.
DNA from individual samples can be sequenced individually (i.e.
singleplex sequencing) or DNA from multiple samples can be pooled
and sequenced as indexed genomic molecules (i.e. multiplex
sequencing) on a single sequencing run, to generate up to several
hundred million reads of DNA sequences. Examples of sequencing
technologies that can be used to obtain the sequence information
according to the present method are described below.
In some embodiments, the methods and apparatus disclosed herein may
employ the following some or all of the operations from the
following sequence: obtain a nucleic acid test sample from a
patient (typically by a non-invasive procedure); process the test
sample in preparation for sequencing; sequence nucleic acids from
the test sample to produce numerous reads (e.g., at least 10,000);
align the reads to portions of a reference sequence/genome and
determine the amount of DNA (e.g., the number of reads) that map to
defined portions the reference sequence (e.g., to defined
chromosomes or chromosome segments); calculate a dose of one or
more of the defined portions by normalizing the amount of DNA
mapping to the defined portions with an amount of DNA mapping to
one or more normalizing chromosomes or chromosome segments selected
for the defined portion; determining whether the dose indicates
that the defined portion is "affected" (e.g., aneuploidy or
mosaic); reporting the determination and optionally converting it
to a diagnosis; using the diagnosis or determination to develop a
plan of treatment, monitoring, or further testing for the
patient.
Determination of Normalizing Sequences in Qualified Samples:
Normalizing Chromosome Sequences and Normalizing Segment
Sequences
Normalizing sequences are identified using sequence information
from a set of qualified samples obtained from subjects known to
comprise cells having a normal copy number for any one sequence of
interest e.g. a chromosome or segment thereof. Determination of
normalizing sequences is outlined in steps 110, 120, 130, 140, and
145 of the embodiment of the method depicted in FIG. 1. The
sequence information obtained from the qualified samples is used
for determining statistically meaningful identification of
chromosomal aneuploidies in test samples (step 165 FIG. 1, and
Examples).
FIG. 1 provides a flow diagram 100 of an embodiment for determining
a CNV of a sequence of interest e.g. a chromosome or segment
thereof, in a biological sample. In some embodiments, a biological
sample is obtained from a subject and comprises a mixture of
nucleic acids contributed by different genomes. The different
genomes can be contributed to the sample by two individuals e.g.
the different genomes are contributed by the fetus and the mother
carrying the fetus. Alternatively, the genomes are contributed to
the sample by aneuploid cancerous cells and normal euploid cells
from the same subject e.g. a plasma sample from a cancer
patient.
Apart from analyzing a patient's test sample, one or more
normalizing chromosomes or one or more normalizing chromosome
segments are selected for each possible chromosome of interest. The
normalizing chromosomes or segments are identified asynchronously
from the normal testing of patient samples, which may take place in
a clinical setting. In other words, the normalizing chromosomes or
segments are identified prior to testing patient samples. The
associations between normalizing chromosomes or segments and
chromosomes or segments of interest are stored for use during
testing. As explained below, such association is typically
maintained over periods of time that span testing of many samples.
The following discussion concerns embodiments for selecting
normalizing chromosomes or chromosome segments for individual
chromosomes or segments of interest.
A set of qualified samples is obtained to identify qualified
normalizing sequences and to provide variance values for use in
determining statistically meaningful identification of CNV in test
samples. In step 110, a plurality of biological qualified samples
are obtained from a plurality of subjects known to comprise cells
having a normal copy number for any one sequence of interest. In
one embodiment, the qualified samples are obtained from mothers
pregnant with a fetus that has been confirmed using cytogenetic
means to have a normal copy number of chromosomes. The biological
qualified samples may be a biological fluid e.g. plasma, or any
suitable sample as described below. In some embodiments, a
qualified sample contains a mixture of nucleic acid molecules e.g.
cfDNA molecules. In some embodiments, the qualified sample is a
maternal plasma sample that contains a mixture of fetal and
maternal cfDNA molecules. Sequence information for normalizing
chromosomes and/or segments thereof is obtained by sequencing at
least a portion of the nucleic acids e.g. fetal and maternal
nucleic acids, using any known sequencing method. Preferably, any
one of the Next Generation Sequencing (NGS) methods described
elsewhere herein is used to sequence the fetal and maternal nucleic
acids as single or clonally amplified molecules. In various
embodiments, the qualified samples are processed as disclosed below
prior to and during sequencing. They may be processed using
apparatus, systems, and kits as disclosed herein.
In step 120, at least a portion of each of all the qualified
nucleic acids contained in the qualified samples are sequenced to
generate millions of sequence reads e.g. 36 bp reads, which are
aligned to a reference genome, e.g. hg18. In some embodiments, the
sequence reads comprise about 20 bp, about 25 bp, about 30 bp,
about 35 bp, about 40 bp, about 45 bp, about 50 bp, about 55 bp,
about 60 bp, about 65 bp, about 70 bp, about 75 bp, about 80 bp,
about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110 bp,
about 120 bp, about 130, about 140 bp, about 150 bp, about 200 bp,
about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450
bp, or about 500 bp. It is expected that technological advances
will enable single-end reads of greater than 500 bp enabling for
reads of greater than about 1000 bp when paired end reads are
generated. In one embodiment, the mapped sequence reads comprise 36
bp. In another embodiment, the mapped sequence reads comprise 25
bp. Sequence reads are aligned to a reference genome, and the reads
that are uniquely mapped to the reference genome are known as
sequence tags. In one embodiment, at least about 3.times.10.sup.6
qualified sequence tags, at least about 5.times.10.sup.6 qualified
sequence tags, at least about 8.times.10.sup.6 qualified sequence
tags, at least about 10.times.10.sup.6 qualified sequence tags, at
least about 15.times.10.sup.6 qualified sequence tags, at least
about 20.times.10.sup.6 qualified sequence tags, at least about
30.times.10.sup.6 qualified sequence tags, at least about
40.times.10.sup.6 qualified sequence tags, or at least about
50.times.10.sup.6 qualified sequence tags comprising between 20 and
40 bp reads are obtained from reads that map uniquely to a
reference genome.
In step 130, all the tags obtained from sequencing the nucleic
acids in the qualified samples are counted to determine a qualified
sequence tag density. In one embodiment the sequence tag density is
determined as the number of qualified sequence tags mapped to the
sequence of interest on the reference genome. In another
embodiment, the qualified sequence tag density is determined as the
number of qualified sequence tags mapped to a sequence of interest
normalized to the length of the qualified sequence of interest to
which they are mapped. Sequence tag densities that are determined
as a ratio of the tag density relative to the length of the
sequence of interest are herein referred to as tag density ratios.
Normalization to the length of the sequence of interest is not
required, and may be included as a step to reduce the number of
digits in a number to simplify it for human interpretation. As all
qualified sequence tags are mapped and counted in each of the
qualified samples, the sequence tag density for a sequence of
interest e.g. a clinically-relevant sequence, in the qualified
samples is determined, as are the sequence tag densities for
additional sequences from which normalizing sequences are
identified subsequently.
In some embodiments, the sequence of interest is a chromosome that
is associated with a complete chromosomal aneuploidy e.g.
chromosome 21, and the qualified normalizing sequence is a complete
chromosome that is not associated with a chromosomal aneuploidy and
whose variation in sequence tag density approximates that of the
sequence (i.e. chromosome) of interest e.g. chromosome 21. The
selected normalizing chromosome(s) may the one or group that best
approximates the variation in sequence tag density of the sequence
of interest. Any one or more of chromosomes 1-22, X, and Y can be a
sequence of interest, and one or more chromosomes can be identified
as the normalizing sequence for each of the any one chromosomes
1-22, X and Y in the qualified samples. The normalizing chromosome
can be an individual chromosome or it can be a group of chromosomes
as described elsewhere herein.
In another embodiment, the sequence of interest is a segment of a
chromosome associated with a partial aneuploidy, e.g. a chromosomal
deletion or insertion, or unbalanced chromosomal translocation, and
the normalizing sequence is a chromosomal segment (or group of
segments) that is not associated with the partial aneuploidy and
whose variation in sequence tag density approximates that of the
chromosome segment associated with the partial aneuploidy. The
selected normalizing chromosome segment(s) may the one or more that
best approximates the variation in sequence tag density of the
sequence of interest. Any one or more segments of any one or more
chromosomes 1-22, X, and Y can be a sequence of interest.
In other embodiments, the sequence of interest is a segment of a
chromosome associated with a partial aneuploidy and the normalizing
sequence is a whole chromosome or chromosomes. In still other
embodiments, the sequence of interest is a whole chromosome
associated with an aneuploidy and the normalizing sequence is a
chromosomal segment or segments that is not associated with the
aneuploidy.
Whether a single sequence or a group of sequences are identified in
the qualified samples as the normalizing sequence(s) for any one or
more sequences of interest, the qualified normalizing sequence may
be chosen to have a variation in sequence tag density that best or
effectively approximates that of the sequence of interest as
determined in the qualified samples. For example, a qualified
normalizing sequence is a sequence that produces the smallest
variability across the qualified samples when used to normalize the
sequence of interest, i.e. the variability of the normalizing
sequence is closest to that of the sequence of interest determined
in qualified samples. Stated another way, the qualified normalizing
sequence is the sequence selected to produce the least variation in
sequence dose (for the sequence of interest) across the qualified
samples. Thus, the process selects a sequence that when used as a
normalizing chromosome is expected to produce the smallest
variability in run-to-run chromosome dose for the sequence of
interest.
The normalizing sequence identified in the qualified samples for
any one or more sequences of interest remains the normalizing
sequence of choice for determining the presence or absence of
aneuploidy in test samples over days, weeks, months, and possibly
years, provided that procedures needed to generate sequencing
libraries, and sequencing the samples are essentially unaltered
over time. As described above, normalizing sequences for
determining the presence of aneuploidies are chosen for (possibly
among other reasons as well) the variability in the number of
sequence tags that are mapped to it among samples e.g. different
samples, and sequencing runs e.g. sequencing runs that occur on the
same day and/or different days, that best approximates the
variability of the sequence of interest for which it is used as a
normalizing parameter. Substantial alterations in these procedures
will affect the number of tags that are mapped to all sequences,
which in turn will determine which one or group of sequences will
have a variability across samples in the same and/or in different
sequencing runs, on the same day or on different days that most
closely approximates that of the sequence(s) of interest, which
would require that the set of normalizing sequences be
re-determined. Substantial alterations in procedures include
changes in the laboratory protocol used for preparing the
sequencing library, which includes changes related to preparing
samples for multiplex sequencing instead of singleplex sequencing,
and changes in sequencing platforms, which include changes in the
chemistry used for sequencing.
In some embodiments, the normalizing sequence chosen to normalize a
particular sequence of interest is a sequence that best
distinguishes one or more qualified, samples from one or more
affected samples, which implies that the normalizing sequence is a
sequence that has the greatest differentiability i.e. the
differentiability of the normalizing sequence is such that it
provides optimal differentiation to a sequence of interest in an
affected test sample to easily distinguish the affected test sample
from other unaffected samples. In other embodiments, the
normalizing sequence is a sequence that has a combination of the
smallest variability and the greatest differentiability.
The level of differentiability can be determined as a statistical
difference between the sequence doses e.g. chromosome doses or
segment doses, in a population of qualified samples and the
chromosome dose(s) in one or more test samples as described below
and shown in the Examples. For example, differentiability can be
represented numerically as a T-test value, which represents the
statistical difference between the chromosome doses in a population
of qualified samples and the chromosome dose(s) in one or more test
samples. Alternatively, differentiability can be represented
numerically as a Normalized Chromosome Value (NCV), which is a
z-score for chromosome doses as long as the distribution for the
NCV is normal. Similarly, differentiability can be represented
numerically as a T-test value, which represents the statistical
difference between the segment doses in a population of qualified
samples and the segment dose(s) in one or more test samples. In the
case where chromosome segments are the sequences of interest,
differentiability of segment doses can be represented numerically
as a Normalized Segment Value (NSV), which is a z-score for
chromosome segment doses as long as the distribution for the NSV is
normal. In determining the z-score, the mean and standard deviation
of chromosome or segment doses in a set of qualified samples can be
used. Alternatively, the mean and standard deviation of chromosome
or segment doses in a training set comprising qualified samples and
affected samples can be used. In other embodiments, the normalizing
sequence is a sequence that has the smallest variability and the
greatest differentiability or an optimal combination of small
variability and large differentiability.
The method identifies sequences that inherently have similar
characteristics and that are prone to similar variations among
samples and sequencing runs, and which are useful for determining
sequence doses in test samples.
Determination of Sequence Doses (i.e. Chromosome Doses or Segment
Doses) in Qualified Samples
In step 140, based on the calculated qualified tag densities, a
qualified sequence dose i.e. a chromosome dose or a segment dose,
for a sequence of interest is determined as the ratio of the
sequence tag density for the sequence of interest and the qualified
sequence tag density for additional sequences from which
normalizing sequences are identified subsequently in step 145. The
identified normalizing sequences are used subsequently to determine
sequence doses in test samples.
In one embodiment, the sequence dose in the qualified samples is a
chromosome dose that is calculated as the ratio of the number of
sequence tags for a chromosome of interest and the number of
sequence tags for a normalizing chromosome sequence in a qualified
sample. The normalizing chromosome sequence can be a single
chromosome, a group of chromosomes, a segment of one chromosome, or
a group of segments from different chromosomes. Accordingly, a
chromosome dose for a chromosome of interest is determined in a
qualified sample as (i) the ratio of the number of tags for a
chromosome of interest and the number of tags for a normalizing
chromosome sequence composed of a single chromosome, (ii) the ratio
of the number of tags for a chromosome of interest and the number
of tags for a normalizing chromosome sequence composed of two or
more chromosomes, (iii) the ratio of the number of tags for a
chromosome of interest and the number of tags for a normalizing
segment sequence composed of a single segment of a chromosome, (iv)
the ratio of the number of tags for a chromosome of interest and
the number of tags for a normalizing segment sequence composed of
two or more segments form one chromosome, or (v) the ratio of the
number of tags for a chromosome of interest and the number of tags
for a normalizing segment sequence composed of two or more segments
of two or more chromosomes. Examples for determining a chromosome
dose for chromosome of interest 21 according to (i)-(v) are as
follows: chromosome doses for chromosome of interest e.g.
chromosome 21, are determined as a ratio of the sequence tag
density of chromosome 21 and the sequence tag density for each of
all the remaining chromosomes i.e. chromosomes 1-20, chromosome 22,
chromosome X, and chromosome Y (i); chromosome doses for chromosome
of interest e.g. chromosome 21, are determined as a ratio of the
sequence tag density of chromosome 21 and the sequence tag density
for all possible combinations of two or more remaining chromosomes
(ii); chromosome doses for chromosome of interest e.g. chromosome
21, are determined as a ratio of the sequence tag density of
chromosome 21 and the sequence tag density for a segment of another
chromosome e.g. chromosome 9 (iii); chromosome doses for chromosome
of interest e.g. chromosome 21, are determined as a ratio of the
sequence tag density of chromosome 21 and the sequence tag density
for two segment of one other chromosome e.g. two segments of
chromosome 9 (iv); and chromosome doses for chromosome of interest
e.g. chromosome 21, are determined as a ratio of the sequence tag
density of chromosome 21 and the sequence tag density for two
segments of two different chromosomes e.g. a segment of chromosome
9 and a segment of chromosome 14.
In another embodiment, the sequence dose in the qualified samples
is a segment dose that is calculated as the ratio of the number of
sequence tags for a segment of interest, that is not a whole
chromosome, and the number of sequence tags for a normalizing
segment sequence in a qualified sample. The normalizing segment
sequence can be, for example, a whole chromosome, a group of whole
chromosomes, a segment of one chromosome, or a group of segments
from different chromosomes. For example, a segment dose for a
segment of interest is determined in a qualified sample as (i) the
ratio of the number of tags for a segment of interest and the
number of tags for a normalizing segment sequence composed of a
single segment of a chromosome, (ii) the ratio of the number of
tags for a segment of interest and the number of tags for a
normalizing segment sequence composed of two or more segments of
one chromosome, or (iii) the ratio of the number of tags for a
segment of interest and the number of tags for a normalizing
segment sequence composed of two or more segments of two or more
different chromosomes.
Chromosome doses for one or more chromosomes of interest are
determined in all qualified samples, and a normalizing chromosome
sequence is identified in step 145. Similarly, segment doses for
one or more segments of interest are determined in all qualified
samples, and a normalizing segment sequence is identified in step
145.
Identification of Normalizing Sequences from Qualified Sequence
Doses
In step 145, a normalizing sequence is identified for a sequence of
interest as the sequence based on the calculated sequence doses
e.g., that results in the smallest variability in sequence dose for
the sequence of interest across all qualified samples. The method
identifies sequences that inherently have similar characteristics
and that are prone to similar variations among samples and
sequencing runs, and which are useful for determining sequence
doses in test samples.
Normalizing sequences for one or more sequences of interest can be
identified in a set of qualified samples, and the sequences that
are identified in the qualified samples are used subsequently to
calculate sequence doses for one or more sequences of interest in
each of the test samples (step 150) to determine the presence or
absence of aneuploidy in each of the test samples. The normalizing
sequence identified for chromosomes or segments of interest may
differ when different sequencing platforms are used and/or when
differences exist in the purification of the nucleic acid that is
to be sequenced and/or preparation of the sequencing library. The
use of normalizing sequences according to the methods described
herein provides specific and sensitive measure of a variation in
copy number of a chromosome or segment thereof irrespective of
sample preparation and/or sequencing platform that is used.
In some embodiments, more than one normalizing sequence is
identified i.e. different normalizing sequences can be determined
for one sequence of interest, and multiple sequence doses can be
determined for one sequence of interest. For example, the
variation, e.g. coefficient of variation, in chromosome dose for
chromosome of interest 21 is least when the sequence tag density of
chromosome 14 is used. However, two, three, four, five, six, seven,
eight or more normalizing sequences can be identified for use in
determining a sequence dose for a sequence of interest in a test
sample. As an example, a second dose for chromosome 21 in any one
test sample can be determined using chromosome 7, chromosome 9,
chromosome 11 or chromosome 12 as the normalizing chromosome
sequence as these chromosomes all have CV close to that for
chromosome 14 (see Example 8, Table 10). Preferably, when a single
chromosome is chosen as the normalizing chromosome sequence for a
chromosome of interest, the normalizing chromosome sequence will be
a chromosome that results in chromosome doses for the chromosome of
interest that has the smallest variability across all samples
tested e.g. qualified samples.
Normalizing Chromosome Sequence as a Normalizing Sequence for
Chromosome(s)
In other embodiments, a normalizing chromosome sequence can be a
single sequence or it can be a group of sequences. For example, in
some embodiments, a normalizing sequence is a group of sequences
e.g. a group of chromosomes, that is identified as the normalizing
sequence for any or more of chromosomes 1-22, X and Y. The group of
chromosomes that compose the normalizing sequence for a chromosome
of interest i.e. a normalizing chromosome sequence, can be a group
of two, three, four, five, six, seven, eight, nine, ten, eleven,
twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen,
nineteen, twenty, twenty-one, or twenty-two chromosomes, and
including or excluding one or both of chromosomes X, and Y. The
group of chromosomes that is identified as the normalizing
chromosome sequence is a group of chromosomes that results in
chromosome doses for the chromosome of interest that has the
smallest variability across all samples tested e.g. qualified
samples. Preferably, individual and groups of chromosomes are
tested together for their ability to best mimic the behavior of the
sequence of interest for which they are chosen as normalizing
chromosome sequences.
In one embodiment, the normalizing sequence for chromosome 21 is
selected from chromosome 9, chromosome 1, chromosome 2, chromosome
3, chromosome 4, chromosome 5, chromosome 6, chromosome 7,
chromosome 8, chromosome 10, chromosome 11, chromosome 12,
chromosome 13, chromosome 14, chromosome 15, chromosome 16, and
chromosome 17. In another embodiment, the normalizing sequence for
chromosome 21 is selected from chromosome 9, chromosome 1,
chromosome 2, chromosome 11, chromosome 12, and chromosome 14.
Alternatively, the normalizing sequence for chromosome 21 is a
group of chromosomes selected from chromosome 9, chromosome 1,
chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome
6, chromosome 7, chromosome 8, chromosome 10, chromosome 11,
chromosome 12, chromosome 13, chromosome 14, chromosome 15,
chromosome 16, and chromosome 17. In another embodiment, the group
of chromosomes is a group selected from chromosome 9, chromosome 1,
chromosome 2, chromosome 11, chromosome 12, and chromosome 14.
In some embodiments, the method is further improved by using a
normalizing sequence that is determined by systematic calculation
of all chromosome doses using each chromosome individually and in
all possible combinations with all remaining chromosomes (see
Example 13). For example, a systematically determined normalizing
chromosome can be determined for each chromosome of interest by
systematically calculating all possible chromosome doses using one
of any of chromosomes 1-22, X, and Y, and combinations of two or
more of chromosomes 1-22, X, and Y to determine which single or
group of chromosomes is the normalizing chromosome that results in
the least variability of the chromosome dose for a chromosome of
interest across a set of qualified samples (see Example 13).
Accordingly, in one embodiment, the systematically calculated
normalizing chromosome sequence for chromosome 21 is a group of
chromosomes consisting of chromosome 4, chromosome 14, chromosome
16, chromosome 20, and chromosome 22. Single or groups of
chromosomes can be determined for all chromosomes in the
genome.
In one embodiment, the normalizing sequence for chromosome 18 is
selected chromosome 8, chromosome 2, chromosome 3, chromosome 4,
chromosome 5, chromosome 6, chromosome 7, chromosome 9, chromosome
10, chromosome 11, chromosome 12, chromosome 13, and chromosome 14.
Preferably, the normalizing sequence for chromosome 18 is selected
from chromosome 8, chromosome 2, chromosome 3, chromosome 5,
chromosome 6, chromosome 12, and chromosome 14. Alternatively, the
normalizing sequence for chromosome 18 is a group of chromosomes
selected from chromosome 8, chromosome 2, chromosome 3, chromosome
4, chromosome 5, chromosome 6, chromosome 7, chromosome 9,
chromosome 10, chromosome 11, chromosome 12, chromosome 13, and
chromosome 14. Preferably, the group of chromosomes is a group
selected from chromosome 8, chromosome 2, chromosome 3, chromosome
5, chromosome 6, chromosome 12, and chromosome 14.
In another embodiment, the normalizing sequence for chromosome 18
is determined by systematic calculation of all possible chromosome
doses using each possible normalizing chromosome individually and
all possible combinations of normalizing chromosomes (as explained
elsewhere herein). Accordingly, in one embodiment, the normalizing
sequence for chromosome 18 is a normalizing chromosome consisting
of the group of chromosomes consisting of chromosome 2, chromosome
3, chromosome 5, and chromosome 7.
In one embodiment, the normalizing sequence for chromosome X is
selected from chromosome 1, chromosome 2, chromosome 3, chromosome
4, chromosome 5, chromosome 6, chromosome 7, chromosome 8,
chromosome 9, chromosome 10, chromosome 11, chromosome 12,
chromosome 13, chromosome 14, chromosome 15, and chromosome 16.
Preferably, the normalizing sequence for chromosome X is selected
from chromosome 2, chromosome 3, chromosome 4, chromosome 5,
chromosome 6 and chromosome 8. Alternatively, the normalizing
sequence for chromosome X is a group of chromosomes selected from
chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome
5, chromosome 6, chromosome 7, chromosome 8, chromosome 9,
chromosome 10, chromosome 11, chromosome 12, chromosome 13,
chromosome 14, chromosome 15, and chromosome 16. Preferably, the
group of chromosomes is a group selected from chromosome 2,
chromosome 3, chromosome 4, chromosome 5, chromosome 6, and
chromosome 8.
In another embodiment, the normalizing sequence for chromosome X is
determined by systematic calculation of all possible chromosome
doses using each possible normalizing chromosome individually and
all possible combinations of normalizing chromosomes (as explained
elsewhere herein). Accordingly, in one embodiment, the normalizing
sequence for chromosome X is a normalizing chromosome consisting of
the group of chromosome 4 and chromosome 8.
In one embodiment, the normalizing sequence for chromosome 13 is a
chromosome selected from chromosome 2, chromosome 3, chromosome 4,
chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome
9, chromosome 10, chromosome 11, chromosome 12, chromosome 14,
chromosome 18, and chromosome 21. Preferably, the normalizing
sequence for chromosome 13 is a chromosome selected from chromosome
2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, and
chromosome 8. In another embodiment, the normalizing sequence for
chromosome 13 is a group of chromosomes selected from chromosome 2,
chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome
7, chromosome 8, chromosome 9, chromosome 10, chromosome 11,
chromosome 12, chromosome 14, chromosome 18, and chromosome 21.
Preferably, the group of chromosomes is a group selected from
chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome
6, and chromosome 8.
In another embodiment, the normalizing sequence for chromosome 13
is determined by systematic calculation of all possible chromosome
doses using each possible normalizing chromosome individually and
all possible combinations of normalizing chromosomes (as explained
elsewhere herein). Accordingly, in one embodiment, the normalizing
sequence for chromosome 13 is a normalizing chromosome comprising
the group of chromosome 4 and chromosome 5. In another embodiment,
the normalizing sequence for chromosome 13 is a normalizing
chromosome consisting of the group of chromosome 4 and chromosome
5.
The variation in chromosome dose for chromosome Y is greater than
30 independently of which normalizing chromosome is used in
determining the chromosome Y dose. Therefore, any one chromosome,
or a group of two or more chromosomes selected from chromosomes
1-22 and chromosome X can be used as the normalizing sequence for
chromosome Y. In one embodiment, the at least one normalizing
chromosome is a group of chromosomes consisting of chromosomes
1-22, and chromosome X. In another embodiment, the group of
chromosomes consists of chromosome 2, chromosome 3, chromosome 4,
chromosome 5, and chromosome 6.
In another embodiment, the normalizing sequence for chromosome Y is
determined by systematic calculation of all possible chromosome
doses using each possible normalizing chromosome individually and
all possible combinations of normalizing chromosomes (as explained
elsewhere herein). Accordingly, in one embodiment, the normalizing
sequence for chromosome Y is a normalizing chromosome comprising
the group of chromosomes consisting of chromosome 4 and chromosome
6. In another embodiment, the normalizing sequence for chromosome Y
is a normalizing chromosome consisting of the group of chromosomes
consisting of chromosome 4 and chromosome 6.
The normalizing sequence used to calculate the dose of different
chromosomes of interest, or of different segments of interest can
be the same or it can be a different normalizing sequence for
different chromosomes or segments of interest, respectively. For
example, the normalizing sequence e.g. a normalizing chromosome
(one or a group) for chromosome of interest A can be the same or it
can be different from the normalizing sequence e.g. a normalizing
chromosome (one or a group) for chromosome of interest B.
The normalizing sequence for a complete chromosome may be a
complete chromosome or a group of complete chromosomes, or it may
be a segment of a chromosome, or a group of segments of one or more
chromosomes.
Normalizing Segment Sequence as a Normalizing Sequence for
Chromosome(s)
In another embodiment, the normalizing sequence for a chromosome
can be a normalizing segment sequence. The normalizing segment
sequence can be a single segment or it can be a group of segments
of one chromosome, or they can be segments from two or more
different chromosomes. A normalizing segment sequence can be
determined by systematic calculation of all combinations of segment
sequences in the genome. For example, a normalizing segment
sequence for chromosome 21 can be a single segment that is bigger
or smaller than the size of chromosome 21, which is approximately
47 Mbp (million base pairs), for example, the normalizing segment
can be a segment from chromosome 9, which is approximately 140 Mbp.
Alternatively, a normalizing sequence for chromosome 21 can be for
example, a combination of segment sequences from two different
chromosome e.g. from chromosome 1, and from chromosome 12.
In one embodiment, the normalizing sequence for chromosome 21 is a
normalizing segment sequence of one segment or of a group of two or
more segments of chromosomes 1-20, 22, X, and Y. In another
embodiment, the normalizing sequence for chromosome 18 is a segment
or groups segments of chromosomes 1-17, 19-22, X, and Y. In another
embodiment, the normalizing sequence for chromosome 13 is a segment
or groups of segments of chromosomes 1-12, 14-22, X, and Y. In
another embodiment, the normalizing sequence for chromosome X is a
segment or groups segments of chromosomes 1-22, and Y. In another
embodiment, the normalizing sequence for chromosome Y is a segment
or group of segments of chromosomes 1-22, and X. Normalizing
segment sequences of single or groups of segments can be determined
for all chromosomes in the genome. The two or more segments of a
normalizing segment sequence can be segments from one chromosome,
or the two or more segments can be segments of two or more
different chromosomes. As described for normalizing chromosome
sequences, a normalizing segment sequence can be the same for two
or more different chromosomes.
Normalizing Segment Sequence as a Normalizing Sequence for
Chromosome Segment(s)
The presence or absence of CNV of a sequence of interest can be
determined when the sequence of interest is a segment of a
chromosome. Variation in the copy number of a chromosome segment
allows for determining the presence or absence of a partial
chromosomal aneuploidy. Described below are examples of partial
chromosomal aneuploidies that are associated with various fetal
abnormalities and disease conditions. The segment of the chromosome
can be of any length. For example, it can range from a kilobase to
hundreds of megabases. The human genome occupies just over 3
billion DNA bases, which can be divided into tens, thousands,
hundreds of thousands and millions of segments of different sizes
of which the copy number can be determined according to the present
method. The normalizing sequence for a segment of a chromosome is a
normalizing segment sequence, which can be a single segment from
any one of the chromosomes 1-22, X and Y, or it can be a group of
segments from any one or more of chromosomes 1-22, X, and Y.
The normalizing sequence for a segment of interest is a sequence
that has a variability across chromosomes and across samples that
is closest to that of the segment of interest. Determination of a
normalizing sequence can be performed as described for determining
the normalizing sequence for a chromosome of interest when the
normalizing sequence is a group of segments of any one or more of
chromosomes 1-22, X and Y. A normalizing segment sequence of one or
a group of segments can be identified by calculating segment doses
using one, and all possible combinations of two or more segments as
normalizing sequences for the segment of interest in each sample of
a set of qualified samples i.e. samples known to be diploid for the
segment of interest, and the normalizing sequence is determined as
that providing a segment dose having the lowest variability for the
segment of interest across all qualified samples, as is described
above for normalizing chromosome sequences.
For example, for a segment of interest that is 1 Mb (megabase), the
remaining 3 million segments (minus the 1 mg segment of interest)
of the approximately 3 Gb human genome can be used individually or
in combination with each other to calculate segment doses for a
segment of interest in a qualified set of sample to determine which
one or group of segments would serve as the normalizing segment
sequence for qualified and test samples. Segments of interest can
vary from about 1000 bases to tens of megabases. Normalizing
segment sequences can be composed of one or more segments of the
same size as that of the sequence of interest. In other embodiment,
the normalizing segment sequence can be composed of segments that
differ from that of the sequence of interest, and/or from each
other. For example, a normalizing segment sequence for a 100,000
base long sequence can be 20,000 bases long, and comprise a
combination of sequences of different lengths e.g. a
7,000+8,000+5,000 bases. As is described elsewhere herein for
normalizing chromosome sequences, normalizing segment sequences can
be determined by systematic calculation of all possible chromosome
and/or segment doses using each possible normalizing chromosome
segment individually and all possible combinations of normalizing
segments (as explained elsewhere herein). Single or groups of
segments can be determined for all segments and/or chromosomes in
the genome.
The normalizing sequence used to calculate the dose of different
chromosome segments of interest can be the same or it can be a
different normalizing sequence for different chromosome segments of
interest. For example, the normalizing sequence e.g. a normalizing
segment (one or a group) for chromosome segment of interest A can
be the same or it can be different from the normalizing sequence
e.g. a normalizing segment (one or a group) for chromosome segment
of interest B.
Normalizing Chromosome Sequence as a Normalizing Sequence for
Chromosome Segment(s)
In another embodiment, variations in copy number of chromosome
segments can be determined using a normalizing chromosome, which
can be a single chromosome or a group of chromosomes as described
above. The normalizing chromosome sequence can be the normalizing
chromosome or group of chromosomes that are identified for the
chromosome of interest in a set of qualified samples by
systematically determining which one or group of chromosomes
provide the lowest variability in the chromosome dose in a set of
qualified samples. For example, to determine the presence or
absence of a partial deletion of chromosome 7, the normalizing
chromosome or group of chromosomes that is used in the analysis for
the partial deletion is the chromosome or group of chromosomes that
are first identified in a qualified set of samples as the
normalizing sequence that provides the lowest chromosome dose for
the entire chromosome 7. As is described elsewhere herein for
normalizing chromosome sequences for chromosomes of interest,
normalizing chromosome sequences for chromosome segments can be
determined by systematic calculation of all possible chromosome
doses using each possible normalizing chromosome individually and
all possible combinations of normalizing chromosomes (as explained
elsewhere herein). Single or groups of chromosomes can be
determined for all segments of chromosomes in the genome. Examples
demonstrating the use of normalizing chromosomes for determining
the presence of a partial chromosomal deletion and for a partial
chromosomal duplication are provided as Examples 17 and 18.
In some embodiments, determination of a CNV of a chromosome segment
is performed by first subdividing the chromosome of interest into
sections or bins of variable length. The bin length can be of at
least about 1 kbp, at least about 10 kbp, at least about 100 kbp,
at least about 1 mbp, at least about 10 mbp, or at least about 100
mbp. The smaller the bin length, the greater the resolution that is
obtained to localize the CNV of the segment in the chromosome of
interest.
Determining the presence or absence of a CNV of a segment of a
chromosome of interest can be obtained by comparing the dose for
each of the bins of the chromosome of interest in a test sample to
a the mean for the corresponding bin dose determined for each bin
of equivalent length in a set of qualified samples. A normalized
bin value for each bin can be calculated as described above for the
normalized segment value as a normalized bin value (NBV), which
relates the bin dose in a test sample to the mean of the of the
corresponding bin dose in a set of qualified samples. The NBV is
calculated as:
.mu..sigma. ##EQU00005##
where {circumflex over (.mu.)}.sub.j and {circumflex over
(.sigma.)}.sub.j, are the estimated mean and standard deviation,
respectively, for the j-th bin dose in a set of qualified samples,
and x.sub.ij is the observed j-th bin dose for test sample i.
Determination of Aneuploidies in Test Samples
Based on the identification of the normalizing sequence(s) in
qualified samples, a sequence dose is determined for a sequence of
interest in a test sample comprising a mixture of nucleic acids
derived from genomes that differ in one or more sequences of
interest.
In step 115, a test sample is obtained from a subject suspected or
known to carry a clinically-relevant CNV of a sequence of interest.
The test sample may be a biological fluid e.g. plasma, or any
suitable sample as described below. As explained, the sample may be
obtained using a non-invasive procedure such as a simple blood
draw. In some embodiments, a test sample contains a mixture of
nucleic acid molecules e.g. cfDNA molecules. In some embodiments,
the test sample is a maternal plasma sample that contains a mixture
of fetal and maternal cfDNA molecules.
In step 125, at least a portion of the test nucleic acids in the
test sample is sequenced as described for the qualified samples to
generate millions of sequence reads e.g. 36 bp reads. As in step
120, the reads generated from sequencing the nucleic acids in the
test sample are uniquely mapped or aligned to a reference genome to
produce tags. As described in step 120, at least about
3.times.10.sup.6 qualified sequence tags, at least about
5.times.10.sup.6 qualified sequence tags, at least about
8.times.10.sup.6 qualified sequence tags, at least about
10.times.10.sup.6 qualified sequence tags, at least about
15.times.10.sup.6 qualified sequence tags, at least about
20.times.10.sup.6 qualified sequence tags, at least about
30.times.10.sup.6 qualified sequence tags, at least about
40.times.10.sup.6 qualified sequence tags, or at least about
50.times.10.sup.6 qualified sequence tags comprising between 20 and
40 bp reads are obtained from reads that map uniquely to a
reference genome. In certain embodiments, the reads produced by
sequencing apparatus are provided in an electronic format.
Alignment is accomplished using computational apparatus as
discussed below. Individual reads are compared against the
reference genome, which is often vast (millions of base pairs) to
identify sites where the reads uniquely correspond with the
reference genome. In some embodiments, the alignment procedure
permits limited mismatch between reads and the reference genome. In
some cases, 1, 2, or 3 base pairs in a read are permitted to
mismatch corresponding base pairs in a reference genome, and yet a
mapping is still made.
In step 135, all or most of the tags obtained from sequencing the
nucleic acids in the test samples are counted to determine a test
sequence tag density using a computational apparatus as described
below. In some embodiments, each read is aligned to a particular
region of the reference genome (a chromosome or segment in most
cases), and the read is converted to a tag by appending site
information to the read. As this process unfolds, the computational
apparatus may keep a running count of the number of tags/reads
mapping to each region of the reference genome (chromosome or
segment in most cases). The counts are stored for each chromosome
or segment of interest and each corresponding normalizing
chromosome or segment.
In certain embodiments, the reference genome has one or more
excluded regions that are part of a true biological genome but are
not included in the reference genome. Reads potentially aligning to
these excluded regions are not counted. Examples of excluded
regions include regions of long repeated sequences, regions of
similarity between X and Y chromosomes, etc.
In some embodiments, the method determines whether to count a tag
more than once when multiple reads align to the same site on a
reference genome or sequence. There may be occasions when two tags
have the same sequence and therefore align to an identical site on
a reference sequence. The method employed to count tags may under
certain circumstances exclude from the count identical tags
deriving from the same sequenced sample. If a disproportionate
number of tags are identical in a given sample, it suggests that
there is a strong bias or other defect in the procedure. Therefore,
in accordance with certain embodiments, the counting method does
not count tags from a given sample that are identical to tags from
the sample that were previously counted.
Various criteria may be set for choosing when to disregard an
identical tag from a single sample. In certain embodiments, a
defined percentage of the tags that are counted must be unique. If
more tags than this threshold are not unique, they are disregarded.
For example, if the defined percentage requires that at least 50%
are unique, identical tags are not counted until the percentage of
unique tags it exceeds 50% for the sample. In other embodiments,
the threshold number of unique tags is at least about 60%. In other
embodiments, the threshold percentage of unique tags is at least
about 75%, or at least about 90%, or at least about 95%, or at
least about 98%, or at least about 99%. A threshold may be set at
90% for chromosome 21. If 30M tags are aligned to chromosome 21,
then at least 27M of them must be unique. If 3M counted tags are
not unique and the 30 million and first tag is not unique, it is
not counted. The choice of the particular threshold or other
criterion used to determine when not to count further identical
tags can be selected using appropriate statistical analysis. One
factor influencing this threshold or other criterion is the
relative amount of sequenced sample to the size of the genome to
which tags can be aligned. Other factors include the size of the
reads and similar considerations.
In one embodiment, the number of test sequence tags mapped to a
sequence of interest is normalized to the known length of a
sequence of interest to which they are mapped to provide a test
sequence tag density ratio. As described for the qualified samples,
normalization to the known length of a sequence of interest is not
required, and may be included as a step to reduce the number of
digits in a number to simplify it for human interpretation. As all
the mapped test sequence tags are counted in the test sample, the
sequence tag density for a sequence of interest e.g. a
clinically-relevant sequence, in the test samples is determined, as
are the sequence tag densities for additional sequences that
correspond to at least one normalizing sequence identified in the
qualified samples.
In step 150, based on the identity of at least one normalizing
sequence in the qualified samples, a test sequence dose is
determined for a sequence of interest in the test sample. In
various embodiments, the test sequence dose is computationally
determined using by manipulating the sequence tag densities of the
sequence of interest and the corresponding normalizing sequence as
described herein. The computational apparatus responsible for this
undertaking will electronically access the association between the
sequence of interest its associated normalizing sequence, which may
be stored in a database, table, graph, or be included as code in
program instructions.
As described elsewhere herein, the at least one normalizing
sequence can be a single sequence or a group of sequences. The
sequence dose for a sequence of interest in a test sample is a
ratio of the sequence tag density determined for the sequence of
interest in the test sample and the sequence tag density of at
least one normalizing sequence determined in the test sample,
wherein the normalizing sequence in the test sample corresponds to
the normalizing sequence identified in the qualified samples for
the particular sequence of interest. For example, if the
normalizing sequence identified for chromosome 21 in the qualified
samples is determined to be a chromosome e.g. chromosome 14, then
the test sequence dose for chromosome 21 (sequence of interest) is
determined as the ratio of the sequence tag density for chromosome
21 in and the sequence tag density for chromosome 14 each
determined in the test sample. Similarly, chromosome doses for
chromosomes 13, 18, X, Y, and other chromosomes associated with
chromosomal aneuploidies are determined. A normalizing sequence for
a chromosome of interest can be one or a group of chromosomes, or
one or a group of chromosome segments. As described previously, a
sequence of interest can be part of a chromosome e.g. a chromosome
segment. Accordingly, the dose for a chromosome segment can be
determined as the ratio of the sequence tag density determined for
the segment in the test sample and the sequence tag density for the
normalizing chromosome segment in the test sample, wherein the
normalizing segment in the test sample corresponds to the
normalizing segment (single or a group of segments) identified in
the qualified samples for the particular segment of interest.
Chromosome segments can range from kilobases (kb) to megabases (Mb)
in size (e.g., about 1 kb to 10 kb, or about 10 kb to 100 kb, or
about 100 kb to 1 Mb).
In step 155, threshold values are derived from standard deviation
values established for qualified sequence doses determined in a
plurality of qualified samples and sequence doses determined for
samples known to be aneuploid for a sequence of interest. Note that
this operation is typically performed asynchronously with analysis
of patient test samples. It may be performed, for example,
concurrently with the selection of normalizing sequences from
qualified samples. Accurate classification depends on the
differences between probability distributions for the different
classes i.e. type of aneuploidy. In some examples, thresholds are
chosen from empirical distribution for each type of aneuploidy e.g.
trisomy 21. Possible threshold values that were established for
classifying trisomy 13, trisomy 18, trisomy 21, and monosomy X
aneuploidies as described in the Examples, which describe the use
of the method for determining chromosomal aneuploidies by
sequencing cfDNA extracted from a maternal sample comprising a
mixture of fetal and maternal nucleic acids. The threshold value
that is determined to distinguish samples affected for an
aneuploidy of a chromosome can be the same or can be different from
the threshold that is determined to distinguish samples affected
for a different aneuploidy. As is shown in the Examples, the
threshold value for each chromosome of interest is determined from
the variability in the dose of the chromosome of interest across
samples and sequencing runs. The less variable the chromosome dose
for any chromosome of interest, the narrower the spread in the dose
for the chromosome of interest across all the unaffected samples,
which are used to set the threshold for determining different
aneuploidies.
Returning to the process flow associated with classifying a patient
test sample, in step 160, the copy number variation of the sequence
of interest is determined in the test sample by comparing the test
sequence dose for the sequence of interest to at least one
threshold value established from the qualified sequence doses. This
operation may be performed by the same computational apparatus
employed to measure sequence tag densities and/or calculate segment
doses.
In step 165, the calculated dose for a test sequence of interest is
compared to that set as the threshold values that are chosen
according to a user-defined "threshold of reliability" to classify
the sample as a "normal" an "affected" or a "no call". The "no
call" samples are samples for which a definitive diagnosis cannot
be made with reliability. Each type of affected sample (e.g.,
trisomy 21, partial trisomy 21, monosomy X) has its own thresholds,
one for calling normal (unaffected) samples and another for calling
affected samples (although in some cases the two thresholds
coincide). As described elsewhere herein, under some circumstances
a no-call can be converted to a call (affected or normal) if fetal
fraction of nucleic acid in the test sample is sufficiently high.
The classification of the test sequence may be reported by the
computational apparatus employed in other operations of this
process flow. In some cases, the classification is reported in an
electronic format and may be displayed, emailed, texted, etc. to
interest persons.
Certain embodiments provide a method for providing prenatal
diagnosis of a fetal chromosomal aneuploidy in a biological sample
comprising fetal and maternal nucleic acid molecules. The diagnosis
is made based on obtaining sequence information sequencing at least
a portion of the mixture of the fetal and maternal nucleic acid
molecules derived from a biological test sample e.g. a maternal
plasma sample, computing from the sequencing data a normalizing
chromosome dose for one or more chromosomes of interest, and/or a
normalizing segment dose for one or more segments of interest, and
determining a statistically significant difference between the
chromosome dose for the chromosome of interest and/or the segment
dose for the segment of interest, respectively, in the test sample
and a threshold value established in a plurality of qualified
(normal) samples, and providing the prenatal diagnosis based on the
statistical difference. As described in step 165 of the method, a
diagnosis of normal or affected is made. A "no call" is provided in
the event that the diagnosis for normal or affected cannot be made
with confidence.
Samples and Sample Processing
Samples
Samples that are used for determining a CNV, e.g. chromosomal
aneuploidies, partial aneuploidies, and the like, can include
samples taken from any cell, tissue, or organ in which copy number
variations for one or more sequences of interest are to be
determined.
Desirably, the samples contain nucleic acids that are that are
present in cells and/or nucleic acids that are "cell-free" (e.g.,
cfDNA).
In some embodiments it is advantageous to obtain cell-free nucleic
acids e.g. cell-free DNA (cfDNA). Cell-free nucleic acids,
including cell-free DNA, can be obtained by various methods known
in the art from biological samples including but not limited to
plasma, serum, and urine (see, e.g., Fan et al., Proc Natl Acad Sci
105:16266-16271 [2008]; Koide et al., Prenatal Diagnosis 25:604-607
[2005]; Chen et al., Nature Med. 2: 1033-1035 [1996]; Lo et al.,
Lancet 350: 485-487 [1997]; Botezatu et al., Clin Chem. 46:
1078-1084, 2000; and Su et al., J. Mol. Diagn. 6: 101-107 [2004]).
To separate cell-free DNA from cells in a sample, various methods
including, but not limited to fractionation, centrifugation (e.g.,
density gradient centrifugation), DNA-specific precipitation, or
high-throughput cell sorting and/or other separation methods can be
used. Commercially available kits for manual and automated
separation of cfDNA are available (Roche Diagnostics, Indianapolis,
Ind., Qiagen, Valencia, Calif., Macherey-Nagel, Duren, Del.).
Biological samples comprising cfDNA have been used in assays to
determine the presence or absence of chromosomal abnormalities e.g.
trisomy 21, by sequencing assays that can detect chromosomal
aneuploidies and/or various polymorphisms.
In various embodiments the cfDNA present in the sample can be
enriched specifically or non-specifically prior to use (e.g., prior
to preparing a sequencing library). Non-specific enrichment of
sample DNA refers to the whole genome amplification of the genomic
DNA fragments of the sample that can be used to increase the level
of the sample DNA prior to preparing a cfDNA sequencing library.
Non-specific enrichment can be the selective enrichment of one of
the two genomes present in a sample that comprises more than one
genome. For example, non-specific enrichment can be selective of
the fetal genome in a maternal sample, which can be obtained by
known methods to increase the relative proportion of fetal to
maternal DNA in a sample. Alternatively, non-specific enrichment
can be the non-selective amplification of both genomes present in
the sample. For example, non-specific amplification can be of fetal
and maternal DNA in a sample comprising a mixture of DNA from the
fetal and maternal genomes. Methods for whole genome amplification
are known in the art. Degenerate oligonucleotide-primed PCR (DOP),
primer extension PCR technique (PEP) and multiple displacement
amplification (MDA) are examples of whole genome amplification
methods. In some embodiments, the sample comprising the mixture of
cfDNA from different genomes is unenriched for cfDNA of the genomes
present in the mixture. In other embodiments, the sample comprising
the mixture of cfDNA from different genomes is non-specifically
enriched for any one of the genomes present in the sample.
The sample comprising the nucleic acid(s) to which the methods
described herein are applied typically comprises a biological
sample ("test sample"), e.g., as described above. In some
embodiments, the nucleic acid(s) to be screened for one or more
CNVs is purified or isolated by any of a number of well-known
methods.
Accordingly, in certain embodiments the sample comprises or
consists of a purified or isolated polynucleotide, or it can
comprise samples such as a tissue sample, a biological fluid
sample, a cell sample, and the like. Suitable biological fluid
samples include, but are not limited to blood, plasma, serum,
sweat, tears, sputum, urine, sputum, ear flow, lymph, saliva,
cerebrospinal fluid, ravages, bone marrow suspension, vaginal flow,
transcervical lavage, brain fluid, ascites, milk, secretions of the
respiratory, intestinal and genitourinary tracts, amniotic fluid,
milk, and leukophoresis samples. In some embodiments, the sample is
a sample that is easily obtainable by non-invasive procedures e.g.
blood, plasma, serum, sweat, tears, sputum, urine, sputum, ear
flow, saliva or feces. In certain embodiments the sample is a
peripheral blood sample, or the plasma and/or serum fractions of a
peripheral blood sample. In other embodiments, the biological
sample is a swab or smear, a biopsy specimen, or a cell culture. In
another embodiment, the sample is a mixture of two or more
biological samples e.g. a biological sample can comprise two or
more of a biological fluid sample, a tissue sample, and a cell
culture sample. As used herein, the terms "blood," "plasma" and
"serum" expressly encompass fractions or processed portions
thereof. Similarly, where a sample is taken from a biopsy, swab,
smear, etc., the "sample" expressly encompasses a processed
fraction or portion derived from the biopsy, swab, smear, etc.
In certain embodiments, samples can be obtained from sources,
including, but not limited to, samples from different individuals,
samples from different developmental stages of the same or
different individuals, samples from different diseased individuals
(e.g., individuals with cancer or suspected of having a genetic
disorder), normal individuals, samples obtained at different stages
of a disease in an individual, samples obtained from an individual
subjected to different treatments for a disease, samples from
individuals subjected to different environmental factors, samples
from individuals with predisposition to a pathology, samples
individuals with exposure to an infectious disease agent (e.g.,
HIV), and the like.
In one illustrative, but non-limiting embodiment, the sample is a
maternal sample that is obtained from a pregnant female, for
example a pregnant woman. In this instance, the sample can be
analyzed using the methods described herein to provide a prenatal
diagnosis of potential chromosomal abnormalities in the fetus. The
maternal sample can be a tissue sample, a biological fluid sample,
or a cell sample. A biological fluid includes, as non-limiting
examples, blood, plasma, serum, sweat, tears, sputum, urine,
sputum, ear flow, lymph, saliva, cerebrospinal fluid, ravages, bone
marrow suspension, vaginal flow, transcervical lavage, brain fluid,
ascites, milk, secretions of the respiratory, intestinal and
genitourinary tracts, and leukophoresis samples.
In another illustrative, but non-limiting embodiment, the maternal
sample is a mixture of two or more biological samples e.g. the
biological sample can comprise two or more of a biological fluid
sample, a tissue sample, and a cell culture sample. In some
embodiments, the sample is a sample that is easily obtainable by
non-invasive procedures e.g. blood, plasma, serum, sweat, tears,
sputum, urine, milk, sputum, ear flow, saliva and feces. In some
embodiments, the biological sample is a peripheral blood sample,
and/or the plasma and serum fractions thereof. In other
embodiments, the biological sample is a swab or smear, a biopsy
specimen, or a sample of a cell culture. As disclosed above, the
terms "blood," "plasma" and "serum" expressly encompass fractions
or processed portions thereof. Similarly, where a sample is taken
from a biopsy, swab, smear, etc., the "sample" expressly
encompasses a processed fraction or portion derived from the
biopsy, swab, smear, etc.
In certain embodiments samples can also be obtained from in vitro
cultured tissues, cells, or other polynucleotide-containing
sources. The cultured samples can be taken from sources including,
but not limited to, cultures (e.g., tissue or cells) maintained in
different media and conditions (e.g., pH, pressure, or
temperature), cultures (e.g., tissue or cells) maintained for
different periods of length, cultures (e.g., tissue or cells)
treated with different factors or reagents (e.g., a drug candidate,
or a modulator), or cultures of different types of tissue and/or
cells.
Methods of isolating nucleic acids from biological sources are well
known and will differ depending upon the nature of the source. One
of skill in the art can readily isolate nucleic acid(s) from a
source as needed for the method described herein. In some
instances, it can be advantageous to fragment the nucleic acid
molecules in the nucleic acid sample. Fragmentation can be random,
or it can be specific, as achieved, for example, using restriction
endonuclease digestion. Methods for random fragmentation are well
known in the art, and include, for example, limited DNAse
digestion, alkali treatment and physical shearing. In one
embodiment, sample nucleic acids are obtained from as cfDNA, which
is not subjected to fragmentation.
In other illustrative embodiments, the sample nucleic acid(s) are
obtained as genomic DNA, which is subjected to fragmentation into
fragments of approximately 300 or more, approximately 400 or more,
or approximately 500 or more base pairs, and to which NGS methods
can be readily applied.
Sequencing Library Preparation
In one embodiment, the methods described herein can utilize next
generation sequencing technologies (NGS), that allow multiple
samples to be sequenced individually as genomic molecules (i.e.
singleplex sequencing) or as pooled samples comprising indexed
genomic molecules (e.g., multiplex sequencing) on a single
sequencing run. These methods can generate up to several hundred
million reads of DNA sequences. In various embodiments the
sequences of genomic nucleic acids, and/or of indexed genomic
nucleic acids can be determined using, for example, the Next
Generation Sequencing Technologies (NGS) described herein. In
various embodiments analysis of the massive amount of sequence data
obtained using NGS can be performed using one or more processors as
described herein.
In various embodiments the use of such sequencing technologies does
not involve the preparation of sequencing libraries.
However, in certain embodiments the sequencing methods contemplated
herein involve the preparation of sequencing libraries. In one
illustrative approach, sequencing library preparation involves the
production of a random collection of adapter-modified DNA fragments
(e.g., polynucleotides) that are ready to be sequenced. Sequencing
libraries of polynucleotides can be prepared from DNA or RNA,
including equivalents, analogs of either DNA or cDNA, for example,
DNA or cDNA that is complementary or copy DNA produced from an RNA
template, by the action of reverse transcriptase. The
polynucleotides may originate in double-stranded form (e.g., dsDNA
such as genomic DNA fragments, cDNA, PCR amplification products,
and the like) or, in certain embodiments, the polynucleotides may
originated in single-stranded form (e.g., ssDNA, RNA, etc.) and
have been converted to dsDNA form. By way of illustration, in
certain embodiments, single stranded mRNA molecules may be copied
into double-stranded cDNAs suitable for use in preparing a
sequencing library. The precise sequence of the primary
polynucleotide molecules is generally not material to the method of
library preparation, and may be known or unknown. In one
embodiment, the polynucleotide molecules are DNA molecules. More
particularly, in certain embodiments, the polynucleotide molecules
represent the entire genetic complement of an organism or
substantially the entire genetic complement of an organism, and are
genomic DNA molecules (e.g., cellular DNA, cell free DNA (cfDNA),
etc.), that typically include both intron sequence and exon
sequence (coding sequence), as well as non-coding regulatory
sequences such as promoter and enhancer sequences. In certain
embodiments, the primary polynucleotide molecules comprise human
genomic DNA molecules, e.g. cfDNA molecules present in peripheral
blood of a pregnant subject.
Preparation of sequencing libraries for some NGS sequencing
platforms is facilitated by the use of polynucleotides comprising a
specific range of fragment sizes. Preparation of such libraries
typically involves the fragmentation of large polynucleotides (e.g.
cellular genomic DNA) to obtain polynucleotides in the desired size
range.
Fragmentation can be achieved by any of a number of methods known
to those of skill in the art. For example, fragmentation can be
achieved by mechanical means including, but not limited to
nebulization, sonication and hydroshear. However mechanical
fragmentation typically cleaves the DNA backbone at C--O, P--O and
C--C bonds resulting in a heterogeneous mix of blunt and 3'- and
5'-overhanging ends with broken C--O, P--O and/C--C bonds (see,
e.g., Alnemri and Liwack, J. Biol. Chem 265:17323-17333 [1990];
Richards and Boyer, J Mol Biol 11:327-240 [1965]) which may need to
be repaired as they may lack the requisite 5'-phosphate for the
subsequent enzymatic reactions e.g. ligation of sequencing
adaptors, that are required for preparing DNA for sequencing.
In contrast, cfDNA, typically exists as fragments of less than
about 300 base pairs and consequently, fragmentation is not
typically necessary for generating a sequencing library using cfDNA
samples.
Typically, whether polynucleotides are forcibly fragmented (e.g.,
fragmented in vitro), or naturally exist as fragments, they are
converted to blunt-ended DNA having 5'-phosphates and 3'-hydroxyl.
Standard protocols e.g. protocols for sequencing using, for
example, the Illumina platform as described elsewhere herein,
instruct users to end-repair sample DNA, to purify the end-repaired
products prior to dA-tailing, and to purify the dA-tailing products
prior to the adaptor-ligating steps of the library preparation.
Various embodiments, of methods of sequence library preparation
described herein obviate the need to perform one or more of the
steps typically mandated by standard protocols to obtain a modified
DNA product that can be sequenced by NGS. An abbreviated method
(ABB method), a 1-step method, and a 2-step method are described
below. Consecutive dA-tailing and adaptor ligation is herein
referred to as the 2-step process. Consecutive dA-tailing, adaptor
ligating, and amplifying is herein referred to as the 1-step
method. In various embodiments the ABB and 2-step methods can be
performed in solution or on a solid surface. In certain embodiments
the 1-step method is performed on a solid surface.
A comparison of a standard method e.g. Illumina, to the abbreviated
method (ABB; Example 2), the 2-step and the 1-step method (Examples
3-6) for preparing DNA molecules for sequencing by NGS according to
embodiments of the present invention is diagrammed in FIG. 2.
Abbreviated Preparation--ABB
In one embodiment, an abbreviated method (ABB method) for the
preparation of a sequence library is provided that comprises the
consecutive steps of end-repairing, dA-tailing and adaptor-ligating
(ABB). In embodiments for preparing sequencing libraries that do
not require the dA-tailing step (see, e.g., protocols for
sequencing using Roche 454 and SOLID.TM.3platforms) the steps of
end-repairing and adaptor-ligating can exclude the purification
step of the end-repaired products prior to the
adaptor-ligating.
The method of preparing sequencing libraries comprising the
consecutive steps of end-repairing, dA tailing and adaptor ligating
is herein referred to as the abbreviated method (ABB), and was
shown to generate sequencing libraries of unexpectedly improved
quality while expediting the analysis of samples (see, e.g.,
Example 2). According to some embodiments of the method, the ABB
method can be performed in solution, as exemplified herein. The ABB
method can also be performed on a solid surface by first
end-repairing and dA-tailing the DNA in solution, and subsequently
binding it to a solid surface as is described elsewhere herein for
the 1-step or 2-step preparation on a solid surface. The three
enzymatic steps, including the step of ligating the adaptors to the
dA-tailed DNA, are performed in the absence of polyethylene glycol.
Published protocols for performing ligation reactions, including
ligating adaptors to DNA, instruct users to perform ligations in
the presence of polyethylene glycol. Applicants determined that the
ligation of the adaptors to the dA-tailed DNA can be performed in
the absence of polyethylene glycol.
In another embodiment, the preparation of the sequencing library
eliminates the need for end-repairing the cfDNA prior to the
dA-tailing step. Applicants have determined that cfDNA, which does
not require to be fragmented, does not need be end-repaired, and
the preparation of the cfDNA sequencing library according to
embodiments of the present invention exclude the end-repair step
and the purification steps to combine enzymatic reactions and
further streamline the preparation of the DNA to be sequenced.
cfDNA exists as a mixture of blunt and 3'- and 5'-overhanging ends
that are generated in vivo by the action of nucleases, which cleave
cellular genomic DNA into cfDNA fragments having termini with a
5'-phosphate and a 3'-hydroxyl group. Elimination of the
end-repairing step selects cfDNA molecules that naturally occur as
blunt-ended molecules, and of cfDNA molecules naturally having 5'
overhanging ends that are filled-in by the polymerase activity of
the enzyme e.g. Klenow Exo-, that is used to attach one or more
deoxynucleotide to the 3'-OH as described below (dA-tailing).
Elimination of the end-repair step of cfDNA selects against cfDNA
molecules that have a 3'-overhanging end (3'-OH). Surprisingly,
exclusion of these 3'-OH cfDNA molecules from the sequencing
library does not affect the representation of genomic sequences in
the library, demonstrating that the end-repair step of cfDNA
molecules may be excluded from the preparation of the sequencing
library (see Examples). In addition to cfDNA, other types of
unrepaired polynucleotides that can be used for preparing
sequencing libraries include DNA molecules resulting from reverse
transcription of RNA molecules e.g. mRNA, siRNA, sRNA, and
unrepaired DNA molecules that are amplicons of DNA synthesized from
phosphorylated primers. When unphosphorylated primers are used, DNA
that is reverse transcribed from RNA, and/or DNA that is amplified
from DNA templates i.e. DNA amplicons, can also be phosphorylated
subsequent to their synthesis by a polynucleotide kinase.
In another embodiment, unrepaired DNA is used for preparing a
sequencing library according to the 2-step method, wherein
end-repair of the DNA is excluded, and unrepaired DNA is subjected
to the two consecutive steps of d-A tailing and adaptor ligating
(see FIG. 2). The 2-step method can be performed in solution or on
a solid surface. When performed in solution, the 2-step method
comprises utilizing DNA obtained from a biological sample,
excluding the step of end-repairing the DNA, and adding a single
deoxynucleotide e.g. deoxyadenosine (A) to the 3'-ends of the
polynucleotides in the sample of unrepaired DNA, for example, by
the activity of certain types of DNA polymerase such as Taq
polymerase or Klenow Exo-polymerase. dA-tailed products, which are
compatible with `T` overhang present on the 3' terminus of each
duplex region of commercially available adaptors are ligated to the
adaptors in a subsequent consecutive step. dA-tailing prevents
self-ligation of both of the blunt-ended polynucleotides to favor
the formation of the adaptor-ligated sequences. Thus, in some
embodiments, unrepaired cfDNA is subjected to the consecutive steps
of dA-tailing and adaptor-ligating, wherein the dA-tailed DNA is
prepared from unrepaired DNA, and is not subjected to a
purification step following the dA-tailing reaction.
Double-stranded adaptors can be ligated to both ends of the
dA-tailed DNA. A set of adaptors having the same sequences, or a
set of two different adaptors can be utilized. In various
embodiments, one or more different sets of same or different
adaptors can also be used. Adaptors can comprise index sequences to
enable multiplex sequencing of the library DNA. Ligation of
adaptors to the dA-tailed DNA can, optionally, be performed in the
absence of polyethylene glycol.
2-Step--Preparation in Solution
In various embodiments, when the 2-step process is performed in
solution, the products of the adaptor ligation reaction can be
purified to remove unligated adaptors, adaptors that may have
ligated to one another. The purification can also select a size
range of templates for cluster generation, that can, optionally, be
preceded by an amplification e.g. a PCR amplification. The ligation
products can be purified by any of a number of methods including,
but not limited to gel electrophoresis, solid-phase reversible
immobilization (SPRI), and the like. In some embodiments, the
purified adaptor-ligated DNA is subjected to an amplification e.g.
PCR amplification, prior to sequencing. Some sequencing platforms
require that the library DNA is further subjected to another
amplification. For example, the Illumina platform requires that a
cluster amplification of library DNA be performed as an integral
part of the sequencing according to the Illumina technology. In
other embodiments, the purified adaptor-ligated DNA is denatured
and the single stranded DNA molecules are attached to the flow cell
of the sequencer. Thus, in some embodiments, the method for
preparing a sequencing library in solution from unrepaired DNA for
NGS sequencing comprises obtaining DNA molecules from a sample; and
performing the consecutive steps of dA tailing and adaptor-ligating
the unrepaired DNA molecules obtained from the sample.
As indicated supra, in various embodiments, these methods of
library preparation are incorporated into a method of determining
copy number variations (CNVs) such as aneuploidies, and the like.
Accordingly, in one illustrative embodiment, a method is provided
for determining the presence or absence of one or more fetal
chromosomal aneuploidies comprising: (a) obtaining a maternal
sample comprising a mixture of fetal and maternal cell-free DNA;
(b) isolating the mixture of fetal and maternal cfDNA from said
sample; (c) preparing a sequencing library from the mixture of
fetal and maternal cfDNA; wherein preparing the library comprises
the consecutive steps of dA-tailing and adaptor ligating the cfDNA,
and wherein preparing the library excludes end-repairing the cfDNA
and the preparation is performed in solution; (d) massively
parallel sequencing at least a portion of the sequencing library to
obtain sequence information for the fetal and maternal cfDNA in the
sample; (e) storing in a computer readable medium, at least
temporarily, the sequence information; (f) using the stored
sequence information to computationally identify a number of
sequence tags for each of one or more chromosomes of interest and
for a normalizing sequence for each of any one or more chromosome
of interest; (g) computationally calculating, using the number of
sequence tags for each of the one or more chromosomes of interest
and the number of sequence tags for the normalizing sequence for
each of the one or more chromosomes of interest, a chromosome dose
for each of the one or more chromosomes of interest; and (h)
comparing the chromosome dose for each of the one or more
chromosomes of interest to a corresponding threshold value for each
of the one or more chromosomes of interest, and thereby determining
the presence or absence of the fetal chromosomal aneuploidy in the
sample, wherein steps (e)-(h) are performed using one or more
processors. This method is exemplified in Examples 3 and 4.
2-Step and 1-Step--Solid Phase Preparation
In some embodiments, the sequencing library is prepared on a solid
surface according to the 2-step method described above for the
preparation of the library in solution. The preparation of the
sequencing library on a solid surface according to the 2-step
method comprises obtaining DNA molecules e.g. cfDNA, from a sample,
and performing the consecutive steps of dA-tailing and adaptor
ligating, where the adaptor-ligating is performed on a solid
surface. Repaired or unrepaired DNA can be used. In some
embodiments, the adaptor-ligated product is detached from the solid
surface, purified, and amplified prior to sequencing. In other
embodiments, the adaptor-ligated product is detached from the solid
surface, purified, and not amplified prior to sequencing. In yet
other embodiments, the adaptor-ligated product is amplified,
detached form the solid surface, and purified. In some embodiments,
the purified product is amplified. In other embodiments, the
purified product is not amplified. The sequencing protocol can
include an amplification e.g. cluster amplification. In various
embodiments the detached adaptor-ligated product is purified prior
to amplification and/or sequencing.
In certain embodiments, the sequencing library is prepared on a
solid surface according to the 1-step method. In various
embodiments the preparation of the sequencing library on a solid
surface according to the 1-step method comprises obtaining DNA
molecules e.g. cfDNA, from a sample, and performing the consecutive
steps of dA-tailing, adaptor ligating, and amplifying, wherein the
adaptor-ligating is performed on a solid surface. The
adaptor-ligated product need not be detached prior to
purification.
FIG. 3 depicts 2-step and 1-step methods for preparing a sequencing
library on a solid surface. Either repaired or unrepaired DNA can
be used for preparing a sequencing library on a solid surface. In
some embodiments, unrepaired DNA is used. Examples of unrepaired
DNA that can be used for preparing a sequencing library on a solid
surface include without limitation cfDNA, DNA that has been reverse
transcribed from RNA using phosphorylated primers, DNA that has
been amplified from DNA template using phosphorylated primers i.e.
phosphorylated DNA amplicons. Examples of repaired DNA that can be
used for preparing a sequencing library on a solid surface include
without limitation cfDNA and fragmented genomic DNA that has been
blunt-ended and phosphorylated i.e. repaired, phosphorylated DNA
generated by reverse transcription of RNA e.g. mRNA, sRNA, siRNA.
In some illustrative embodiments, unrepaired cfDNA obtained from a
maternal sample is used for preparing the sequencing library.
Preparation of a sequencing library on a solid surface comprises
coating the solid surface with a first partner of a two-part
conjugate, modifying a first adaptor by attaching the second
partner of the two part conjugate to the adaptor, and immobilizing
the adaptor on the solid surface by the binding interaction of the
first and second partners of the two-part conjugate. For example,
preparation of sequencing libraries on a solid surface can comprise
attaching a polypeptide, polynucleotide or small molecule to an end
of a library adaptor, which polypeptide, polynucleotide or small
molecule is capable of forming a conjugate complex with a
polypeptide, a polynucleotide or small molecule that is immobilized
on a solid surface. Solid surfaces that can be used for
immobilizing polypeptides, polynucleotides or small molecules
include without limitation plastic, paper, membranes, filters,
chips, pins or glass slides, silica or polymer beads (e.g.
polypropylene, polystyrene, polycarbonate), 2D or 3D molecular
scaffolds, or any support for solid-phase synthesis of polypeptides
or polynucleotides.
Bonding between polypeptide-polypeptide,
polypeptide-polynucleotide, polypeptide-small molecule, and
polynucleotide-polynucleotide conjugates can be covalent or
noncovalent. Preferably, conjugate complexes are bound by
noncovalent bonds. For example, conjugates that can be used in
preparing sequencing libraries on a solid surface include without
limitation streptavidin-biotin conjugates, antibody-antigen
conjugates, and ligand-receptor conjugates. Examples of
polypeptide-polynucleotide conjugates that can be used in preparing
sequencing libraries on a solid surface include without limitation
DNA-binding protein-DNA conjugates. Examples of
polynucleotide-polynucleotide conjugates that can be used in
preparing sequencing libraries on a solid surface include without
limitation oligodT-oligoA, and oligodT-oligodA. Examples of
polypeptide-small molecule and polynucleotide-small molecule
conjugates include streptavidin-biotin.
According to embodiments (1-step and 2-step) of the solid surface
method as shown in FIG. 3, the solid surface of the vessel used for
preparing the sequencing library e.g. a polypropylene PCR tube or
96-well plate, is coated with a polypeptide e.g. streptavidin. The
end of a first set of adaptors is modified by attaching a small
molecule e.g. a biotin molecule, and the biotinylated adaptors are
bound to the streptavidin on the solid surface (1). Subsequently,
the unrepaired or the repaired DNA is ligated to the
streptavidin-bound biotinylated adaptor, thereby immobilizing it to
the solid surface (2). The second set of adaptors is ligated to the
immobilized DNA (3).
2-Step--Preparation on Solid Phase
In one embodiment, the 2-step method is performed using unrepaired
DNA e.g. cfDNA, for preparing the sequencing library on a solid
surface. The unrepaired DNA is dA-tailed by attaching a single
nucleotide base e.g. dA, to the 3' ends of the unrepaired DNA e.g.
cfDNA, strands. Optionally, multiple nucleotide bases can be
attached to the unrepaired DNA. The mixture comprising the
dA-tailed DNA is added to the adaptors immobilized on the solid
surface, to which it is ligated. The steps of dA-tailing and
adaptor-ligating the DNA are consecutive i.e. purification of the
dA-tailed product is not performed (as shown in FIG. 2 for the
2-step method). As described above, the adaptors may have overhangs
that are complementary to overhangs on the unrepaired DNA molecule.
Subsequently, a second set of adaptors is added to the
DNA-biotinylated adaptor complex to provide an adaptor-ligated DNA
library. Optionally, repaired DNA is used for preparing the
library. Repaired DNA can be genomic DNA that has been fragmented
and subjected to in vitro enzymatic repair of 3' and 5' ends. In
one embodiment, DNA e.g. maternal cfDNA, is end-repaired, dA-tailed
and adaptor-ligated to adaptors immobilized on a solid surface in
consecutive steps of end-repairing, dA-tailing and adaptor-ligating
as described for the abbreviated method performed in solution.
In certain embodiments utilizing the 2-step process, the
adaptor-ligated DNA is detached from the solid surface by chemical
or physical means e.g. heat, UV light etc. (4a in FIG. 2), is
purified (5 in FIG. 2), and optionally, it is subjected to an
amplification in solution prior to beginning the sequencing
process. In other embodiments, the adaptor-ligated DNA is not
amplified. Absent amplification, the adaptors ligated to the DNA
can be constructed to comprise sequences that hybridize to
oligonucleotides present on the flow cell of a sequencer (Kozarewa
et al., Nat Methods 6:291-295 [2009]), and an amplification that
introduces sequences for hybridizing the library DNA to the flow
cell of a sequencer is avoided. The library of adaptor-ligated DNA
is subjected to massively parallel sequencing (6 in FIG. 2) as
described for the adaptor-ligated DNA created in solution. In some
embodiments, sequencing is massively parallel sequencing using
sequencing-by-synthesis with reversible dye terminators. In other
embodiments, sequencing is massively parallel sequencing using
sequencing-by-ligation. The sequencing process may include a
solid-phase amplification e.g. cluster amplification, as described
elsewhere herein.
Thus, in various embodiments, the method for preparing a sequencing
library on a solid surface from unrepaired DNA for NGS can comprise
obtaining DNA molecules from a sample; and performing the
consecutive steps of dA tailing and adaptor-ligating the unrepaired
DNA molecules, where adaptor-ligating is performed on a solid
phase. In certain embodiments, the adaptors can include index
sequences, to allow for multiplexing the sequencing of multiple
samples within a single reaction vessel e.g. a channel of a flow
cell. As described above, the DNA molecules can be cfDNA molecules,
they can be DNA molecules transcribed from RNA, they can be
amplicons of DNA molecules, and the like.
As indicated supra, in various embodiments, these methods of
library preparation are incorporated into a method of determining
copy number variations (CNVs) such as aneuploidies, and the like.
Thus, in some embodiments the method for preparing a sequencing
library on a solid surface from unrepaired cfDNA is incorporated
into a method for analyzing a maternal sample to determine the
presence or absence of a fetal chromosomal aneuploidy. Accordingly,
in one embodiment, a method is provided for determining the
presence or absence of one or more fetal chromosomal aneuploidies
comprising: (a) obtaining a maternal sample comprising a mixture of
fetal and maternal cell-free DNA; (b) isolating the mixture of
fetal and maternal cfDNA from said sample; (c) preparing a
sequencing library from the mixture of fetal and maternal cfDNA;
wherein preparing the library comprises the consecutive steps of
dA-tailing and adaptor ligating the cfDNA, where preparing the
library excludes end-repairing the cfDNA and the preparation is
performed on a solid surface; (d) massively parallel sequencing at
least a portion of the sequencing library to obtain sequence
information for the fetal and maternal cfDNA in the sample; (e)
storing in a computer readable medium, at least temporarily, the
sequence information; (f) using the stored sequence information to
computationally identify a number of sequence tags for each of one
or more chromosomes of interest and for a normalizing sequence for
each of any one or more chromosome of interest; (g) computationally
calculating, using the number of sequence tags for each of the one
or more chromosomes of interest and the number of sequence tags for
the normalizing sequence for each of the one or more chromosomes of
interest, a chromosome dose for each of the one or more chromosomes
of interest; and (h) comparing the chromosome dose for each of the
one or more chromosomes of interest to a corresponding threshold
value for each of the one or more chromosomes of interest, and
thereby determining the presence or absence of the fetal
chromosomal aneuploidy in the sample, wherein steps (e)-(h) are
performed using one or more processors. The sample can be a
biological fluid sample e.g. plasma, serum, urine and saliva. In
some embodiments, the sample is a maternal blood sample, or the
plasma or serum fraction thereof. This method is exemplified in
Example 4.
1-Step--Preparation on Solid Phase
In another embodiment, unrepaired DNA is dA-tailed, but the
dA-tailed product is not purified prior to amplification such that
the steps of dA-tailing, adaptor-ligating and amplifying are
performed consecutively or sequentially. Consecutive dA-tailing,
adaptor ligating and amplifying followed by purification prior to
sequencing, is herein referred to as the 1-step process. The 1-step
method can be performed on a solid surface (see, e.g., FIG. 3). The
steps of attaching the first set of adaptors to a solid surface
(1), ligating unrepaired and dA-tailed DNA to the surface-bound
adaptors (2), and ligating the second set of adaptors to the
surface-bound DNA (3), can be performed as described for the 2-step
method above. In the 1-step method, however, the adaptor-ligated
surface-bound DNA can be amplified while attached to the solid
surface (4b in FIG. 2). Subsequently, the resulting library of
adaptor-ligated DNA created on a solid surface is detached and
purified (5 in FIG. 2) prior to being subjected to massively
parallel sequencing as described for the adaptor-ligated DNA
created in solution. In some embodiments, sequencing is massively
parallel sequencing using sequencing-by-synthesis with reversible
dye terminators. In other embodiments, sequencing is massively
parallel sequencing using sequencing-by-ligation.
Accordingly, in some embodiments, the a method is provided for
preparing a sequencing library for NGS sequencing, by performing
the steps comprising obtaining DNA molecules from a sample; and
performing the consecutive steps of dA-tailing, adaptor-ligating,
and amplifying the DNA molecules, where the adaptor-ligating is
performed on a solid surface. As described for the 2-step method,
in various embodiments, the adaptors can include index sequences to
allow for multiplexing the sequencing of multiple samples within a
single reaction vessel e.g. a channel of a flow cell.
In some embodiments, the DNA can be repaired. The DNA molecules can
be cfDNA molecules, they can be DNA molecules transcribed from RNA,
or the DNA molecules can be amplicons of DNA molecules.
Adaptor-ligation is performed as described above. Excess unligated
adaptors can be washed from the immobilized adaptor-ligated DNA;
reagents required for an amplification are added to the immobilized
adaptor-ligated DNA, which is subjected to cycles of amplification
e.g. PCR amplification, as is known in the art. In other
embodiments, the adaptor-ligated DNA is not amplified. Absent
amplification the adaptor-ligated DNA can be removed from the solid
surface by chemical or physical means e.g. heat, UV light etc.
Absent amplification, the adaptors ligated to the DNA can comprise
sequences that hybridize to oligonucleotides present on the flow
cell of the sequencer (Kozarewa et al., Nat Methods 6:291-295
[2009]).
In various embodiments the sample can be a biological fluid sample
(e.g., blood, plasma, serum, urine, cerebrospinal fluid, amniotic
fluid, saliva, and the like). In some embodiments the method for
preparing a sequencing library on a solid surface from unrepaired
cfDNA is included as a step in a method for analyzing a maternal
sample to determine the presence or absence of a fetal chromosomal
aneuploidy.
Accordingly, in one embodiment, a method is provided for
determining the presence or absence of one or more fetal
chromosomal aneuploidies comprising: (a) obtaining a maternal
sample comprising a mixture of fetal and maternal cell-free DNA;
(b) isolating the mixture of fetal and maternal cfDNA from said
sample; (c) preparing a sequencing library from the mixture of
fetal and maternal cfDNA; wherein preparing the library comprises
the consecutive steps of dA-tailing, adaptor ligating, and
amplifying the cfDNA, and wherein the preparation is performed on a
solid surface; (d) massively parallel sequencing at least a portion
of the sequencing library to obtain sequence information for the
fetal and maternal cfDNA in the sample; (e) storing in a computer
readable medium, at least temporarily, the sequence information;
(f) using the stored sequence information to computationally
identify a number of sequence tags for each of one or more
chromosomes of interest and for a normalizing sequence for each of
any one or more chromosome of interest; (g) computationally
calculating, using the number of sequence tags for each of the one
or more chromosomes of interest and the number of sequence tags for
the normalizing sequence for each of the one or more chromosomes of
interest, a chromosome dose for each of the one or more chromosomes
of interest; and (h) comparing the chromosome dose for each of the
one or more chromosomes of interest to a corresponding threshold
value for each of the one or more chromosomes of interest, and
thereby determining the presence or absence of the fetal
chromosomal aneuploidy in the sample, wherein steps (e)-(h) are
performed using one or more processors. In some embodiments, the
DNA is end-repaired. In other embodiments, preparing the library
excludes end-repairing the cfDNA. This method is exemplified in
Examples 5 and 6.
The processes for preparing sequencing libraries as described above
are applicable to methods of sample analyses including without
limitation methods for determining copy number variations (CNV),
and methods for determining the presence or absence of
polymorphisms of any sequence of interest in samples containing
single genomes and in samples containing mixtures of at least two
genomes, which are known or are suspected to differ in one or more
sequence of interest.
An amplification of the adaptor-ligated product prepared on a solid
phase or in solution may be required to introduce to the adaptor
ligated template molecules the oligonucleotide sequences that are
required for hybridization to the flow cell or other surface
present in some of the NGS platforms. The contents of an
amplification reaction are known by one skilled in the art and
include appropriate substrates (such as dNTPs), enzymes (e.g. a DNA
polymerase) and buffer components required for an amplification
reaction. Optionally, amplification of adaptor-ligated
polynucleotides can be omitted. Generally amplification reactions
require at least two amplification primers e.g. primer
oligonucleotides, that can be identical or different and that can
include an "adaptor-specific portion" capable of annealing to a
primer-binding sequence in the polynucleotide molecule to be
amplified (or the complement thereof if the template is viewed as a
single strand) during the annealing step.
Once formed, the library of templates prepared according to the
methods described above can be used for solid-phase nucleic acid
amplification that may be required by some NGS platforms. The term
"solid-phase amplification" as used herein refers to any nucleic
acid amplification reaction carried out on or in association with a
solid support such that all or a portion of the amplified products
are immobilized on the solid support as they are formed. In
particular embodiments, the term encompasses solid-phase polymerase
chain reaction (solid-phase PCR) and solid phase isothermal
amplification which are reactions analogous to standard solution
phase amplification, except that one or both of the forward and
reverse amplification primers is/are immobilized on the solid
support. Solid phase PCR also includes systems such as emulsions,
where one primer is anchored to a bead and the other is in free
solution, and colony formation in solid phase gel matrices wherein
one primer is anchored to the surface, and one is in free
solution.
In various embodiments following amplification, and sequencing
libraries can be analyzed by microfluidic capillary electrophoresis
to ensure that the library is free of adaptor dimers or single
stranded DNA. The library of template polynucleotide molecules is
particularly suitable for use in solid phase sequencing methods. In
addition to providing templates for solid-phase sequencing and
solid-phase PCR, library templates provide templates for whole
genome amplification.
Marker Nucleic Acids for Tracking and Verifying Sample
Integrity
In various embodiments verification of the integrity of the samples
and sample tracking can be accomplished by sequencing mixtures of
sample genomic nucleic acids e.g. cfDNA, and accompanying marker
nucleic acids that have been introduced into the samples, e.g.,
prior to processing.
Marker nucleic acids can be combined with the test sample (e.g.,
biological source sample) and subjected to processes that include,
for example, one or more of the steps of fractionating the
biological source sample e.g. obtaining an essentially cell-free
plasma fraction from a whole blood sample, purifying nucleic acids
from a fractionated e.g. plasma, or unfractionated biological
source sample e.g. a tissue sample, and sequencing. In some
embodiments, sequencing comprises preparing a sequencing library.
The sequence or combination of sequences of the marker molecules
that are combined with a source sample is chosen to be unique to
the source sample. In some embodiments, the unique marker molecules
in a sample all have the same sequence. In other embodiments, the
unique marker molecules in a sample are a plurality of sequences,
e.g., a combination of two, three, four, five, six, seven, eight,
nine, ten, fifteen, twenty, or more different sequences.
In one embodiment, the integrity of a sample can be verified using
a plurality of marker nucleic acid molecules having identical
sequences. Alternatively, the identity of a sample can be verified
using a plurality of marker nucleic acid molecules that have at
least two, at least three, at least four, at least five, at least
six, at least seven, at least eight, at least nine, at least ten,
at least 11, at least 12, at least 13, at least 14, at least 15, at
least 16, at least 17 m, at least 18, at least 19, at least 20, at
least 25, at least 30, at least 35, at least 40, at least 50, or
more different sequences. Verification of the integrity of the
plurality of biological samples i.e. two or more biological
samples, requires that each of the two or more samples be marked
with marker nucleic acids that have sequences that are unique to
each of the plurality of test sample that is being marked. For
example, a first sample can be marked with a marker nucleic acid
having sequence A, and a second sample can be marked with a marker
nucleic acid having sequence B. Alternatively, a first sample can
be marked with marker nucleic acid molecules all having sequence A,
and a second sample can be marked with a mixture of sequences B and
C, wherein sequences A, B and C are marker molecules having
different sequences.
The marker nucleic acid(s) can be added to the sample at any stage
of sample preparation that occurs prior to library preparation (if
libraries are to be prepared) and sequencing. In one embodiment,
marker molecules can be combined with an unprocessed source sample.
For example, the marker nucleic acid can be provided in a
collection tube that is used to collect a blood sample.
Alternatively, the marker nucleic acids can be added to the blood
sample following the blood draw. In one embodiment, the marker
nucleic acid is added to the vessel that is used to collect a
biological fluid sample e.g. the marker nucleic acid(s) are added
to a blood collection tube that is used to collect a blood sample.
In another embodiment, the marker nucleic acid(s) are added to a
fraction of the biological fluid sample. For example, the marker
nucleic acid is added to the plasma and/or serum fraction of a
blood sample e.g. a maternal plasma sample. In yet another
embodiment, the marker molecules are added to a purified sample
e.g. a sample of nucleic acids that have been purified from a
biological sample. For example, the marker nucleic acid is added to
a sample of purified maternal and fetal cfDNA. Similarly, the
marker nucleic acids can be added to a biopsy specimen prior to
processing the specimen. In some embodiments, the marker nucleic
acids can be combined with a carrier that delivers the marker
molecules into the cells of the biological sample. Cell-delivery
carriers include pH-sensitive and cationic liposomes.
In various embodiments, the marker molecules have antigenomic
sequences, that are sequences that are absent from the genome of
the biological source sample. In an exemplary embodiment, the
marker molecules that are used to verify the integrity of a human
biological source sample have sequences that are absent from the
human genome. In an alternative embodiment, the marker molecules
have sequences that are absent from the source sample and from any
one or more other known genomes. For example, the marker molecules
that are used to verify the integrity of a human biological source
sample have sequences that are absent from the human genome and
from the mouse genome. The alternative allows for verifying the
integrity of a test sample that comprises two or more genomes. For
example, the integrity of a human cell-free DNA sample obtained
from a subject affected by a pathogen e.g. a bacterium, can be
verified using marker molecules having sequences that are absent
from both the human genome and the genome of the affecting
bacterium. Sequences of genomes of numerous pathogens e.g.
bacteria, viruses, yeasts, fungi, protozoa etc., are publicly
available on the world wide web at ncbi.nlm.nih.gov/genomes. In
another embodiment, marker molecules are nucleic acids that have
sequences that are absent from any known genome. The sequences of
marker molecules can be randomly generated algorithmically.
In various embodiments the marker molecules can be
naturally-occurring deoxyribonucleic acids (DNA), ribonucleic acids
or artificial nucleic acid analogs (nucleic acid mimics) including
peptide nucleic acids (PMA), morpholino nucleic acid, locked
nucleic acids, glycol nucleic acids, and threose nucleic acids,
which are distinguished from naturally-occurring DNA or RNA by
changes to the backbone of the molecule or DNA mimics that do not
have a phosphodiester backbone. The deoxyribonucleic acids can be
from naturally-occurring genomes or can be generated in a
laboratory through the use of enzymes or by solid phase chemical
synthesis. Chemical methods can also be used to generate the DNA
mimics that are not found in nature. Derivatives of DNA are that
are available in which the phosphodiester linkage has been replaced
but in which the deoxyribose is retained include but are not
limited to DNA mimics having backbones formed by thioformacetal or
a carboxamide linkage, which have been shown to be good structural
DNA mimics. Other DNA mimics include morpholino derivatives and the
peptide nucleic acids (PNA), which contain an
N-(2-aminoethyl)glycine-based pseudopeptide backbone (Ann Rev
Biophys Biomol Struct 24:167-183 [1995]). PNA is an extremely good
structural mimic of DNA (or of ribonucleic acid [RNA]), and PNA
oligomers are able to form very stable duplex structures with
Watson-Crick complementary DNA and RNA (or PNA) oligomers, and they
can also bind to targets in duplex DNA by helix invasion (Mol
Biotechnol 26:233-248 [2004]. Another good structural mimic/analog
of DNA analog that can be used as a marker molecule is
phosphorothioate DNA in which one of the non-bridging oxygens is
replaced by a sulfur. This modification reduces the action of endo-
and exonucleases 2 including 5' to 3' and 3' to 5' DNA POL 1
exonuclease, nucleases S1 and P1, RNases, serum nucleases and snake
venom phosphodiesterase.
The length of the marker molecules can be distinct or indistinct
from that of the sample nucleic acids i.e. the length of the marker
molecules can be similar to that of the sample genomic molecules,
or it can be greater or smaller than that of the sample genomic
molecules. The length of the marker molecules is measured by the
number of nucleotide or nucleotide analog bases that constitute the
marker molecule. Marker molecules having lengths that differ from
those of the sample genomic molecules can be distinguished from
source nucleic acids using separation methods known in the art. For
example, differences in the length of the marker and sample nucleic
acid molecules can be determined by electrophoretic separation e.g.
capillary electrophoresis. Size differentiation can be advantageous
for quantifying and assessing the quality of the marker and sample
nucleic acids. Preferably, the marker nucleic acids are shorter
than the genomic nucleic acids, and of sufficient length to exclude
them from being mapped to the genome of the sample. For example, as
a 30 base human sequence is needed to uniquely map it to a human
genome. Accordingly in certain embodiments, marker molecules used
in sequencing bioassays of human samples should be at least 30 bp
in length.
The choice of length of the marker molecule is determined primarily
by the sequencing technology that is used to verify the integrity
of a source sample. The length of the sample genomic nucleic acids
being sequenced can also be considered. For example, some
sequencing technologies employ clonal amplification of
polynucleotides, which can require that the genomic polynucleotides
that are to be clonally amplified be of a minimum length. For
example, sequencing using the Illumina GAII sequence analyzer
includes an in vitro clonal amplification by bridge PCR (also known
as cluster amplification) of polynucleotides that have a minimum
length of 110 bp, to which adaptors are ligated to provide a
nucleic acid of at least 200 bp and less than 600 bp that can be
clonally amplified and sequenced. In some embodiments, the length
of the adaptor-ligated marker molecule is between about 200 bp and
about 600 bp, between about 250 bp and 550 bp, between about 300 bp
and 500 bp, or between about 350 and 450. In other embodiments, the
length of the adaptor-ligated marker molecule is about 200 bp. For
example, when sequencing fetal cfDNA that is present in a maternal
sample, the length of the marker molecule can be chosen to be
similar to that of fetal cfDNA molecules. Thus, in one embodiment,
the length of the marker molecule used in an assay that comprises
massively parallel sequencing of cfDNA in a maternal sample to
determine the presence or absence of a fetal chromosomal
aneuploidy, can be about 150 bp, about 160 bp, 170 bp, about 180
bp, about 190 bp or about 200 bp; preferably, the marker molecule
is about 170 bp. Other sequencing approaches e.g. SOLiD sequencing,
Polony Sequencing and 454 sequencing use emulsion PCR to clonally
amplify DNA molecules for sequencing, and each technology dictates
the minimum and the maximum length of the molecules that are to be
amplified. The length of marker molecules to be sequenced as
clonally amplified nucleic acids can be up to about 600 bp. In some
embodiments, the length of marker molecules to be sequenced can be
greater than 600 bp.
Single molecule sequencing technologies, that do not employ clonal
amplification of molecules, and are capable of sequencing nucleic
acids over a very broad range of template lengths, in most
situations do not require that the molecules to be sequenced be of
any specific length. However, the yield of sequences per unit mass
is dependent on the number of 3' end hydroxyl groups, and thus
having relatively short templates for sequencing is more efficient
than having long templates. If starting with nucleic acids longer
than 1000 nt, it is generally advisable to shear the nucleic acids
to an average length of 100 to 200 nt so that more sequence
information can be generated from the same mass of nucleic acids.
Thus, the length of the marker molecule can range from tens of
bases to thousands of bases. The length of marker molecules used
for single molecule sequencing can be up to about 25 bp, up to
about 50 bp, up to about 75 bp, up to about 100 bp, up to about 200
bp, up to about 300 bp, up to about 400 bp, up to about 500 bp, up
to about 600 bp, up to about 700 bp, up to about 800 bp, up to
about 900 bp, up to about 1000 bp, or more in length.
The length chosen for a marker molecule is also determined by the
length of the genomic nucleic acid that is being sequenced. For
example, cfDNA circulates in the human bloodstream as genomic
fragments of cellular genomic DNA. Fetal cfDNA molecules found in
the plasma of pregnant women are generally shorter than maternal
cfDNA molecules (Chan et al., Clin Chem 50:8892 [2004]). Size
fractionation of circulating fetal DNA has confirmed that the
average length of circulating fetal DNA fragments is <300 bp,
while maternal DNA has been estimated to be between about 0.5 and 1
Kb (Li et al., Clin Chem, 50: 1002-1011 [2004]). These findings are
consistent with those of Fan et al., who determined using NGS that
fetal cfDNA is rarely >340 bp (Fan et al., Clin Chem
56:1279-1286 [2010]). DNA isolated from urine with a standard
silica-based method consists of two fractions, high molecular
weight DNA, which originates from shed cells and low molecular
weight (150-250 base pair) fraction of transrenal DNA (Tr-DNA)
(Botezatu et al., Clin Chem. 46: 1078-1084, 2000; and Su et al., J
Mol. Diagn. 6: 101-107, 2004). The application of newly developed
technique for isolation of cell-free nucleic acids from body fluids
to the isolation of transrenal nucleic acids has revealed the
presence in urine of DNA and RNA fragments much shorter than 150
base pairs (U.S. Patent Application Publication No. 20080139801).
In embodiments, wherein cfDNA is the genomic nucleic acid that is
sequenced, marker molecules that are chosen can be up to about the
length of the cfDNA. For example, the length of marker molecules
used in maternal cfDNA samples to be sequenced as single nucleic
acid molecules or as clonally amplified nucleic acids can be
between about 100 bp and 600. In other embodiments, the sample
genomic nucleic acids are fragments of larger molecules. For
example, a sample genomic nucleic acid that is sequenced is
fragmented cellular DNA. In embodiments, when fragmented cellular
DNA is sequenced, the length of the marker molecules can be up to
the length of the DNA fragments. In some embodiments, the length of
the marker molecules is at least the minimum length required for
mapping the sequence read uniquely to the appropriate reference
genome. In other embodiments, the length of the marker molecule is
the minimum length that is required to exclude the marker molecule
from being mapped to the sample reference genome.
In addition, marker molecules can be used to verify samples that
are not assayed by nucleic acid sequencing, and that can be
verified by common biotechniques other than sequencing e.g.
real-time PCR.
Sample Controls (e.g., in Process Positive Controls for Sequencing
and/or Analysis).
In various embodiments marker sequences introduced into the
samples, e.g., as described above, can function as positive
controls to verity the verify the accuracy and efficacy of
sequencing and subsequent processing and analysis.
Accordingly, compositions and method for providing an in-process
positive control (IPC) for sequencing DNA in a sample are provided.
In certain embodiments, positive controls are provided for
sequencing cfDNA in a sample comprising a mixture of genomes are
provided. An IPC can be used to relate baseline shifts in sequence
information obtained from different sets of samples e.g. samples
that are sequenced at different times on different sequencing runs.
Thus, for example, an IPC can relate the sequence information
obtained for a maternal test sample to the sequence information
obtained from a set of qualified samples that were sequenced at a
different time.
Similarly, in the case of segment analysis, an IPC can relate the
sequence information obtained from a subject for particular
segment(s) to the sequence obtained from a set of qualified samples
(of similar sequences) that were sequenced at a different time. In
certain embodiments an IPC can relate the sequence information
obtained from a subject for particular cancer-related loci to the
sequence information obtained from a set of qualified samples
(e.g., from a known amplification/deletion, and the like).
In addition, IPCs can be used as markers to track sample(s) through
the sequencing process. IPCs can also provide a qualitative
positive sequence dose value e.g. NCV, for one or more aneuploidies
of chromosomes of interest e.g. trisomy 21, trisomy 13, trisomy 18
to provide proper interpretation, and to ensure the dependability
and accuracy of the data. In certain embodiments IPCs can be
created to comprise nucleic acids from male and female genomes to
provide doses for chromosomes X and Y in a maternal sample to
determine whether the fetus is male.
The type and the number of in-process controls depends on the type
or nature of the test needed. For example, for a test requiring the
sequencing of DNA from a sample comprising a mixture of genomes to
determine whether a chromosomal aneuploidy exists, the in-process
control can comprise DNA obtained from a sample known comprising
the same chromosomal aneuploidy that is being tested. In some
embodiments, the IPC includes DNA from a sample known to comprise
an aneuploidy of a chromosome of interest. For example, the IPC for
a test to determine the presence or absence of a fetal trisomy e.g.
trisomy 21, in a maternal sample comprises DNA obtained from an
individual with trisomy 21. In some embodiments, the IPC comprises
a mixture of DNA obtained from two or more individuals with
different aneuploidies. For example, for a test to determine the
presence or absence of trisomy 13, trisomy 18, trisomy 21, and
monosomy X, the IPC comprises a combination of DNA samples obtained
from pregnant women each carrying a fetus with one of the trisomies
being tested. In addition to complete chromosomal aneuploidies,
IPCs can be created to provide positive controls for tests to
determine the presence or absence of partial aneuploidies.
An IPC that serves as the control for detecting a single aneuploidy
can be created using a mixture of cellular genomic DNA obtained
from a two subjects one being the contributor of the aneuploid
genome. For example, an IPC that is created as a control for a test
to determine a fetal trisomy e.g. trisomy 21, can be created by
combining genomic DNA from a male or female subject carrying the
trisomic chromosome with genomic DNA with a female subject known
not to carry the trisomic chromosome. Genomic DNA can be extracted
from cells of both subjects, and sheared to provide fragments of
between about 100-400 bp, between about 150-350 bp, or between
about 200-300 bp to simulate the circulating cfDNA fragments in
maternal samples. The proportion of fragmented DNA from the subject
carrying the aneuploidy e.g. trisomy 21, is chosen to simulate the
proportion of circulating fetal cfDNA found in maternal samples to
provide an IPC comprising a mixture of fragmented DNA comprising
about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, of
DNA from the subject carrying the aneuploidy. The IPC can comprise
DNA from different subjects each carrying a different aneuploidy.
For example, the IPC can comprise about 80% of the unaffected
female DNA, and the remaining 20% can be DNA from three different
subjects each carrying a trisomic chromosome 21, a trisomic
chromosome 13, and a trisomic chromosome 18. The mixture of
fragmented DNA is prepared for sequencing. Processing of the
mixture of fragmented DNA can comprise preparing a sequencing
library, which can be sequenced using any massively parallel
methods in singleplex or multiplex fashion. Stock solutions of the
genomic IPC can be stored and used in multiple diagnostic
tests.
Alternatively the IPC can be created using cfDNA obtained from a
mother known to carry a fetus with a known chromosomal aneuploidy.
For example, cfDNA can be obtained from a pregnant woman carrying a
fetus with trisomy 21. The cfDNA is extracted from the maternal
sample, and cloned into a bacterial vector and grown in bacteria to
provide an ongoing source of the IPC. The DNA can be extracted from
the bacterial vector using restriction enzymes. Alternatively, the
cloned cfDNA can be amplified by e.g. PCR. The IPC DNA can be
processed for sequencing in the same runs as the cfDNA from the
test samples that are to be analyzed for the presence or absence of
chromosomal aneuploidies.
While the creation of IPCs is described above with respect to
trisomys, it will be appreciated that IPCs can be created to
reflect other partial aneuploidies including for example, various
segment amplification and/or deletions. Thus, for example, where
various cancers are known to be associated with particular
amplifications (e.g., breast cancer associated with 20Q13) IPCs can
be created that incorporate those known amplifications.
Sequencing Methods
As indicated above, the prepared samples (e.g., Sequencign
Libraries) are sequenced as part of the procedure for identifying
copy number variation(s). Any of a number of sequencing
technologies can be utilized.
Some sequencing technologies are available commercially, such as
the sequencing-by-hybridization platform from Affymetrix Inc.
(Sunnyvale, Calif.) and the sequencing-by-synthesis platforms from
454 Life Sciences (Bradford, Conn.), Illumina/Solexa (Hayward,
Calif.) and Helicos Biosciences (Cambridge, Mass.), and the
sequencing-by-ligation platform from Applied Biosystems (Foster
City, Calif.), as described below. In addition to the single
molecule sequencing performed using sequencing-by-synthesis of
Helicos Biosciences, other single molecule sequencing technologies
include, but are not limited to, the SMRT.TM. technology of Pacific
Biosciences, the ION TORRENT.TM. technology, and nanopore
sequencing developed for example, by Oxford Nanopore
Technologies.
While the automated Sanger method is considered as a `first
generation` technology, Sanger sequencing including the automated
Sanger sequencing, can also be employed in the methods described
herein. Additional suitable sequencing methods include, but are not
limited to nucleic acid imaging technologies e.g. atomic force
microscopy (AFM) or transmission electron microscopy (TEM).
Illustrative sequencing technologies are described in greater
detail below.
In one illustrative, but non-limiting, embodiment, the methods
described herein comprise obtaining sequence information for the
nucleic acids in a test sample e.g. cfDNA in a maternal sample,
cfDNA or cellular DNA in a subject being screened for a cancer, and
the like, using single molecule sequencing technology of the
Helicos True Single Molecule Sequencing (tSMS) technology (e.g. as
described in Harris T. D. et al., Science 320:106-109 [2008]). In
the tSMS technique, a DNA sample is cleaved into strands of
approximately 100 to 200 nucleotides, and a polyA sequence is added
to the 3' end of each DNA strand. Each strand is labeled by the
addition of a fluorescently labeled adenosine nucleotide. The DNA
strands are then hybridized to a flow cell, which contains millions
of oligo-T capture sites that are immobilized to the flow cell
surface. In certain embodiments the templates can be at a density
of about 100 million templates/cm.sup.2. The flow cell is then
loaded into an instrument, e.g., HeliScope.TM. sequencer, and a
laser illuminates the surface of the flow cell, revealing the
position of each template. A CCD camera can map the position of the
templates on the flow cell surface. The template fluorescent label
is then cleaved and washed away. The sequencing reaction begins by
introducing a DNA polymerase and a fluorescently labeled
nucleotide. The oligo-T nucleic acid serves as a primer. The
polymerase incorporates the labeled nucleotides to the primer in a
template directed manner. The polymerase and unincorporated
nucleotides are removed. The templates that have directed
incorporation of the fluorescently labeled nucleotide are discerned
by imaging the flow cell surface. After imaging, a cleavage step
removes the fluorescent label, and the process is repeated with
other fluorescently labeled nucleotides until the desired read
length is achieved. Sequence information is collected with each
nucleotide addition step. Whole genome sequencing by single
molecule sequencing technologies excludes or typically obviates
PCR-based amplification in the preparation of the sequencing
libraries, and the methods allow for direct measurement of the
sample, rather than measurement of copies of that sample.
In another illustrative, but non-limiting embodiment, the methods
described herein comprise obtaining sequence information for the
nucleic acids in the test sample e.g. cfDNA in a maternal test
sample, cfDNA or cellular DNA in a subject being screened for a
cancer, and the like, using the 454 sequencing (Roche) (e.g. as
described in Margulies, M. et al. Nature 437:376-380 [2005]). 454
sequencing typically involves two steps. In the first step, DNA is
sheared into fragments of approximately 300-800 base pairs, and the
fragments are blunt-ended. Oligonucleotide adaptors are then
ligated to the ends of the fragments. The adaptors serve as primers
for amplification and sequencing of the fragments. The fragments
can be attached to DNA capture beads, e.g., streptavidin-coated
beads using, e.g., Adaptor B, which contains 5'-biotin tag. The
fragments attached to the beads are PCR amplified within droplets
of an oil-water emulsion. The result is multiple copies of clonally
amplified DNA fragments on each bead. In the second step, the beads
are captured in wells (e.g., picoliter-sized wells). Pyrosequencing
is performed on each DNA fragment in parallel. Addition of one or
more nucleotides generates a light signal that is recorded by a CCD
camera in a sequencing instrument. The signal strength is
proportional to the number of nucleotides incorporated.
Pyrosequencing makes use of pyrophosphate (PPi) which is released
upon nucleotide addition. PPi is converted to ATP by ATP
sulfurylase in the presence of adenosine 5' phosphosulfate.
Luciferase uses ATP to convert luciferin to oxyluciferin, and this
reaction generates light that is measured and analyzed.
In another illustrative, but non-limiting, embodiment, the methods
described herein comprises obtaining sequence information for the
nucleic acids in the test sample e.g. cfDNA in a maternal test
sample, cfDNA or cellular DNA in a subject being screened for a
cancer, and the like, using the SOLiD.TM. technology (Applied
Biosystems). In SOLiD.TM. sequencing-by-ligation, genomic DNA is
sheared into fragments, and adaptors are attached to the 5' and 3'
ends of the fragments to generate a fragment library.
Alternatively, internal adaptors can be introduced by ligating
adaptors to the 5' and 3' ends of the fragments, circularizing the
fragments, digesting the circularized fragment to generate an
internal adaptor, and attaching adaptors to the 5' and 3' ends of
the resulting fragments to generate a mate-paired library. Next,
clonal bead populations are prepared in microreactors containing
beads, primers, template, and PCR components. Following PCR, the
templates are denatured and beads are enriched to separate the
beads with extended templates. Templates on the selected beads are
subjected to a 3' modification that permits bonding to a glass
slide. The sequence can be determined by sequential hybridization
and ligation of partially random oligonucleotides with a central
determined base (or pair of bases) that is identified by a specific
fluorophore. After a color is recorded, the ligated oligonucleotide
is cleaved and removed and the process is then repeated.
In another illustrative, but non-limiting, embodiment, the methods
described herein comprise obtaining sequence information for the
nucleic acids in the test sample e.g. cfDNA in a maternal test
sample, cfDNA or cellular DNA in a subject being screened for a
cancer, and the like, using the single molecule, real-time
(SMRT.TM.) sequencing technology of Pacific Biosciences. In SMRT
sequencing, the continuous incorporation of dye-labeled nucleotides
is imaged during DNA synthesis. Single DNA polymerase molecules are
attached to the bottom surface of individual zero-mode wavelength
detectors (ZMW detectors) that obtain sequence information while
phospholinked nucleotides are being incorporated into the growing
primer strand. A ZMW detector comprises a confinement structure
that enables observation of incorporation of a single nucleotide by
DNA polymerase against a background of fluorescent nucleotides that
rapidly diffuse in an out of the ZMW (e.g., in microseconds). It
typically takes several milliseconds to incorporate a nucleotide
into a growing strand. During this time, the fluorescent label is
excited and produces a fluorescent signal, and the fluorescent tag
is cleaved off. Measurement of the corresponding fluorescence of
the dye indicates which base was incorporated. The process is
repeated to provide a sequence.
In another illustrative, but non-limiting embodiment, the methods
described herein comprise obtaining sequence information for the
nucleic acids in the test sample e.g. cfDNA in a maternal test
sample, cfDNA or cellular DNA in a subject being screened for a
cancer, and the like, using nanopore sequencing (e.g. as described
in Soni G V and Meller A. Clin Chem 53: 1996-2001 [2007]). Nanopore
sequencing DNA analysis techniques are developed by a number of
companies, including, for example, Oxford Nanopore Technologies
(Oxford, United Kingdom), Sequenom, NABsys, and the like. Nanopore
sequencing is a single-molecule sequencing technology whereby a
single molecule of DNA is sequenced directly as it passes through a
nanopore. A nanopore is a small hole, typically of the order of 1
nanometer in diameter. Immersion of a nanopore in a conducting
fluid and application of a potential (voltage) across it results in
a slight electrical current due to conduction of ions through the
nanopore. The amount of current that flows is sensitive to the size
and shape of the nanopore. As a DNA molecule passes through a
nanopore, each nucleotide on the DNA molecule obstructs the
nanopore to a different degree, changing the magnitude of the
current through the nanopore in different degrees. Thus, this
change in the current as the DNA molecule passes through the
nanopore provides a read of the DNA sequence.
In another illustrative, but non-limiting, embodiment, the methods
described herein comprises obtaining sequence information for the
nucleic acids in the test sample e.g. cfDNA in a maternal test
sample, cfDNA or cellular DNA in a subject being screened for a
cancer, and the like, using the chemical-sensitive field effect
transistor (chemFET) array (e.g., as described in U.S. Patent
Application Publication No. 2009/0026082). In one example of this
technique, DNA molecules can be placed into reaction chambers, and
the template molecules can be hybridized to a sequencing primer
bound to a polymerase. Incorporation of one or more triphosphates
into a new nucleic acid strand at the 3' end of the sequencing
primer can be discerned as a change in current by a chemFET. An
array can have multiple chemFET sensors. In another example, single
nucleic acids can be attached to beads, and the nucleic acids can
be amplified on the bead, and the individual beads can be
transferred to individual reaction chambers on a chemFET array,
with each chamber having a chemFET sensor, and the nucleic acids
can be sequenced.
In another embodiment, the present method comprises obtaining
sequence information for the nucleic acids in the test sample e.g.
cfDNA in a maternal test sample, using the Halcyon Molecular's
technology, which uses transmission electron microscopy (TEM). The
method, termed Individual Molecule Placement Rapid Nano Transfer
(IMPRNT), comprises utilizing single atom resolution transmission
electron microscope imaging of high-molecular weight (150 kb or
greater) DNA selectively labeled with heavy atom markers and
arranging these molecules on ultra-thin films in ultra-dense (3 nm
strand-to-strand) parallel arrays with consistent base-to-base
spacing. The electron microscope is used to image the molecules on
the films to determine the position of the heavy atom markers and
to extract base sequence information from the DNA. The method is
further described in PCT patent publication WO 2009/046445. The
method allows for sequencing complete human genomes in less than
ten minutes.
In another embodiment, the DNA sequencing technology is the Ion
Torrent single molecule sequencing, which pairs semiconductor
technology with a simple sequencing chemistry to directly translate
chemically encoded information (A, C, G, T) into digital
information (0, 1) on a semiconductor chip. In nature, when a
nucleotide is incorporated into a strand of DNA by a polymerase, a
hydrogen ion is released as a byproduct. Ion Torrent uses a
high-density array of micro-machined wells to perform this
biochemical process in a massively parallel way. Each well holds a
different DNA molecule. Beneath the wells is an ion-sensitive layer
and beneath that an ion sensor. When a nucleotide, for example a C,
is added to a DNA template and is then incorporated into a strand
of DNA, a hydrogen ion will be released. The charge from that ion
will change the pH of the solution, which can be detected by Ion
Torrent's ion sensor. The sequencer--essentially the world's
smallest solid-state pH meter--calls the base, going directly from
chemical information to digital information. The Ion personal
Genome Machine (PGM.TM.) sequencer then sequentially floods the
chip with one nucleotide after another. If the next nucleotide that
floods the chip is not a match. No voltage change will be recorded
and no base will be called. If there are two identical bases on the
DNA strand, the voltage will be double, and the chip will record
two identical bases called. Direct detection allows recordation of
nucleotide incorporation in seconds.
In another embodiment, the present method comprises obtaining
sequence information for the nucleic acids in the test sample e.g.
cfDNA in a maternal test sample, using sequencing by hybridization.
Sequencing-by-hybridization comprises contacting the plurality of
polynucleotide sequences with a plurality of polynucleotide probes,
wherein each of the plurality of polynucleotide probes can be
optionally tethered to a substrate. The substrate might be flat
surface comprising an array of known nucleotide sequences. The
pattern of hybridization to the array can be used to determine the
polynucleotide sequences present in the sample. In other
embodiments, each probe is tethered to a bead, e.g., a magnetic
bead or the like. Hybridization to the beads can be determined and
used to identify the plurality of polynucleotide sequences within
the sample.
In another embodiment, the present method comprises obtaining
sequence information for the nucleic acids in the test sample e.g.
cfDNA in a maternal test sample, by massively parallel sequencing
of millions of DNA fragments using Illumina's
sequencing-by-synthesis and reversible terminator-based sequencing
chemistry (e.g. as described in Bentley et al., Nature 6:53-59
[2009]). Template DNA can be genomic DNA e.g. cfDNA. In some
embodiments, genomic DNA from isolated cells is used as the
template, and it is fragmented into lengths of several hundred base
pairs. In other embodiments, cfDNA is used as the template, and
fragmentation is not required as cfDNA exists as short fragments.
For example fetal cfDNA circulates in the bloodstream as fragments
approximately 170 base pairs (bp) in length (Fan et al., Clin Chem
56:1279-1286 [2010]), and no fragmentation of the DNA is required
prior to sequencing. Illumina's sequencing technology relies on the
attachment of fragmented genomic DNA to a planar, optically
transparent surface on which oligonucleotide anchors are bound.
Template DNA is end-repaired to generate 5'-phosphorylated blunt
ends, and the polymerase activity of Klenow fragment is used to add
a single A base to the 3' end of the blunt phosphorylated DNA
fragments. This addition prepares the DNA fragments for ligation to
oligonucleotide adapters, which have an overhang of a single T base
at their 3' end to increase ligation efficiency. The adapter
oligonucleotides are complementary to the flow-cell anchors. Under
limiting-dilution conditions, adapter-modified, single-stranded
template DNA is added to the flow cell and immobilized by
hybridization to the anchors. Attached DNA fragments are extended
and bridge amplified to create an ultra-high density sequencing
flow cell with hundreds of millions of clusters, each containing
1,000 copies of the same template. In one embodiment, the randomly
fragmented genomic DNA e.g. cfDNA, is amplified using PCR before it
is subjected to cluster amplification. Alternatively, an
amplification-free genomic library preparation is used, and the
randomly fragmented genomic DNA e.g. cfDNA is enriched using the
cluster amplification alone (Kozarewa et al., Nature Methods
6:291-295 [2009]). The templates are sequenced using a robust
four-color DNA sequencing-by-synthesis technology that employs
reversible terminators with removable fluorescent dyes.
High-sensitivity fluorescence detection is achieved using laser
excitation and total internal reflection optics. Short sequence
reads of about 20-40 bp e.g. 36 bp, are aligned against a
repeat-masked reference genome and unique mapping of the short
sequence reads to the reference genome are identified using
specially developed data analysis pipeline software.
Non-repeat-masked reference genomes can also be used. Whether
repeat-masked or non-repeat-masked reference genomes are used, only
reads that map uniquely to the reference genome are counted. After
completion of the first read, the templates can be regenerated in
situ to enable a second read from the opposite end of the
fragments. Thus, either single-end or paired end sequencing of the
DNA fragments can be used. Partial sequencing of DNA fragments
present in the sample is performed, and sequence tags comprising
reads of predetermined length e.g. 36 bp, are mapped to a known
reference genome are counted. In one embodiment, the reference
genome sequence is the NCBI36/hg18 sequence, which is available on
the world wide web at
genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105).
Alternatively, the reference genome sequence is the GRCh37/hg19,
which is available on the world wide web at
genome.ucsc.edu/cgi-bin/hgGateway. Other sources of public sequence
information include GenBank, dbEST, dbSTS, EMBL (the European
Molecular Biology Laboratory), and the DDBJ (the DNA Databank of
Japan). A number of computer algorithms are available for aligning
sequences, including without limitation BLAST (Altschul et al.,
1990), BLITZ (MPsrch) (Sturrock & Collins, 1993), FASTA (Person
& Lipman, 1988), BOWTIE (Langmead et al., Genome Biology
10:R25.1-R25.10 [2009]), or ELAND (Illumina, Inc., San Diego,
Calif., USA). In one embodiment, one end of the clonally expanded
copies of the plasma cfDNA molecules is sequenced and processed by
bioinformatic alignment analysis for the Illumina Genome Analyzer,
which uses the Efficient Large-Scale Alignment of Nucleotide
Databases (ELAND) software.
In some embodiments of the methods described herein, the mapped
sequence tags comprise sequence reads of about 20 bp, about 25 bp,
about 30 bp, about 35 bp, about 40 bp, about 45 bp, about 50 bp,
about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp,
about 80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp,
about 110 bp, about 120 bp, about 130, about 140 bp, about 150 bp,
about 200 bp, about 250 bp, about 300 bp, about 350 bp, about 400
bp, about 450 bp, or about 500 bp. It is expected that
technological advances will enable single-end reads of greater than
500 bp enabling for reads of greater than about 1000 bp when paired
end reads are generated. In one embodiment, the mapped sequence
tags comprise sequence reads that are 36 bp. Mapping of the
sequence tags is achieved by comparing the sequence of the tag with
the sequence of the reference to determine the chromosomal origin
of the sequenced nucleic acid (e.g. cfDNA) molecule, and specific
genetic sequence information is not needed. A small degree of
mismatch (0-2 mismatches per sequence tag) may be allowed to
account for minor polymorphisms that may exist between the
reference genome and the genomes in the mixed sample.
A plurality of sequence tags are typically obtained per sample. In
some embodiments, at least about 3.times.10.sup.6 sequence tags, at
least about 5.times.10.sup.6 sequence tags, at least about
8.times.10.sup.6 sequence tags, at least about 10.times.10.sup.6
sequence tags, at least about 15.times.10.sup.6 sequence tags, at
least about 20.times.10.sup.6 sequence tags, at least about
30.times.10.sup.6 sequence tags, at least about 40.times.10.sup.6
sequence tags, or at least about 50.times.10.sup.6 sequence tags
comprising between 20 and 40 bp reads e.g. 36 bp, are obtained from
mapping the reads to the reference genome per sample. In one
embodiment, all the sequence reads are mapped to all regions of the
reference genome. In one embodiment, the tags that have been mapped
to all regions e.g. all chromosomes, of the reference genome are
counted, and the CNV i.e. the over- or under-representation of a
sequence of interest e.g. a chromosome or portion thereof, in the
mixed DNA sample is determined. The method does not require
differentiation between the two genomes.
The accuracy required for correctly determining whether a CNV e.g.
aneuploidy, is present or absent in a sample, is predicated on the
variation of the number of sequence tags that map to the reference
genome among samples within a sequencing run (inter-chromosomal
variability), and the variation of the number of sequence tags that
map to the reference genome in different sequencing runs
(inter-sequencing variability). For example, the variations can be
particularly pronounced for tags that map to GC-rich or GC-poor
reference sequences. Other variations can result from using
different protocols for the extraction and purification of the
nucleic acids, the preparation of the sequencing libraries, and the
use of different sequencing platforms. The present method uses
sequence doses (chromosome doses, or segment doses) based on the
knowledge of normalizing sequences (normalizing chromosome
sequences or normalizing segment sequences), to intrinsically
account for the accrued variability stemming from interchromosomal
(intra-run), and inter-sequencing (inter-run) and
platform-dependent variability. Chromosome doses are based on the
knowledge of a normalizing chromosome sequence, which can be
composed of a single chromosome, or of two or more chromosomes
selected from chromosomes 1-22, X, and Y. Alternatively,
normalizing chromosome sequences can be composed of a single
chromosome segment, or of two or more segments of one chromosome or
of two or more chromosomes. Segment doses are based on the
knowledge of a normalizing segment sequence, which can be composed
of a single segment of any one chromosome, or of two or more
segments of any two or more of chromosomes 1-22, X, and Y.
Singleplex Sequencing
FIG. 4 illustrates a flow chart of an embodiment of the method
whereby marker nucleic acids are combined with source sample
nucleic acids of a single sample to assay for a genetic abnormality
while determining the integrity of the biological source sample. In
step 410, a biological source sample comprising genomic nucleic
acids is obtained. In step 420, marker nucleic acids are combined
with the biological source sample to provide a marked sample. A
sequencing library of a mixture of clonally amplified source sample
genomic and marker nucleic acids is prepared in step 430, and the
library is sequenced in a massively parallel fashion in step 440 to
provide sequencing information pertaining to the source genomic and
marker nucleic acids of the sample. Massively parallel sequencing
methods provide sequencing information as sequence reads, which are
mapped to one or more reference genomes to generate sequence tags
that can be analyzed. In step 450, all sequencing information is
analyzed, and based on the sequencing information pertaining to the
marker molecules, the integrity of the source sample is verified in
step 460. Verification of source sample integrity is accomplished
by determining a correspondence between the sequencing information
obtained for the maker molecule at step 450 and the known sequence
of the marker molecule that was added to the original source sample
at step 420. The same process can be applied to multiple samples
that are sequenced separately, with each sample comprising
molecules having sequences unique to the sample i.e. one sample is
marked with a unique marker molecule and it is sequenced separately
from other samples in a flow cell or slide of a sequencer. If the
integrity of the sample is verified, the sequencing information
pertaining to the genomic nucleic acids of the sample can be
analyzed to provide information e.g. about the status of the
subject from which the source sample was obtained. For example, if
the integrity of the sample is verified, the sequencing information
pertaining to the genomic nucleic acids is analyzed to determine
the presence or absence of a chromosomal abnormality. If the
integrity of the sample is not verified, the sequencing information
is disregarded.
The method depicted in FIG. 4 is also applicable to bioassays that
comprise singleplex sequencing of single molecules e.g. tSMS by
Helicos, SMRT by Pacific Biosciences, BASE by Oxford Nanopore, and
other technologies such as that suggested by IBM, which do not
require preparation of libraries.
Multiplex Sequencing
The large number of sequence reads that can be obtained per
sequencing run permits the analysis of pooled samples i.e.
multiplexing, which maximizes sequencing capacity and reduces
workflow. For example, the massively parallel sequencing of eight
libraries performed using the eight lane flow cell of the Illumina
Genome Analyzer can be multiplexed to sequence two or more samples
in each lane such that 16, 24, 32 etc. or more samples can be
sequenced in a single run. Parallelizing sequencing for multiple
samples i.e. multiplex sequencing, requires the incorporation of
sample-specific index sequences, also known as barcodes, during the
preparation of sequencing libraries. Sequencing indexes are
distinct base sequences of about 5, about 10, about 15, about 20
about 25, or more bases that are added at the 3' end of the genomic
and marker nucleic acid. The multiplexing system enables sequencing
of hundreds of biological samples within a single sequencing run.
The preparation of indexed sequencing libraries for sequencing of
clonally amplified sequences can be performed by incorporating the
index sequence into one of the PCR primers used for cluster
amplification. Alternatively, the index sequence can be
incorporated into the adaptor, which is ligated to the cfDNA prior
to the PCR amplification. Indexed libraries for single molecule
sequencing can be created by incorporating the index sequence at
the 3' end of the marker and genomic molecule or 5' to the addition
of a sequence needed for hybridization to the flow cell anchors
e.g. addition of the polyA tail for single molecule sequencing
using the tSMS. Sequencing of the uniquely marked indexed nucleic
acids provides index sequence information that identifies samples
in the pooled sample libraries, and sequence information of marker
molecules correlates sequencing information of the genomic nucleic
acids to the sample source. In embodiments wherein the multiple
samples are sequenced individually i.e. singleplex sequencing,
marker and genomic nucleic acid molecules of each sample need only
be modified to contain the adaptor sequences as required by the
sequencing platform and exclude the indexing sequences.
FIG. 5 provides a flowchart of an embodiment 500 of the method for
verifying the integrity of samples that are subjected to a
multistep multiplex sequencing bioassay i.e. nucleic acids from
individual samples are combined and sequenced as a complex mixture.
In step 510, a plurality of biological source samples each
comprising genomic nucleic acids is obtained. In step 520, unique
marker nucleic acids are combined with each of the biological
source samples to provide a plurality of uniquely marked samples. A
sequencing library of sample genomic and marker nucleic acids is
prepared in step 530 for each of the uniquely marked samples.
Library preparation of samples that are destined to undergo
multiplexed sequencing comprises the incorporation of distinct
indexing tags into the sample and marker nucleic acids of each of
the uniquely marked samples to provide samples whose source nucleic
acid sequences can be correlated with the corresponding marker
nucleic acid sequences and identified in complex solutions. In
embodiments of the method comprising marker molecules that can be
enzymatically modified, e.g. DNA, indexing molecules can be
incorporated at the 3' of the sample and marker molecules by
ligating sequenceable adaptor sequences comprising the indexing
sequences. In embodiments of the method comprising marker molecules
that cannot be enzymatically modified, e.g. DNA analogs that do not
have a phosphate backbone, indexing sequences are incorporated at
the 3' of the analog marker molecules during synthesis. Sequencing
libraries of two or more samples are pooled and loaded on the flow
cell of the sequencer where they are sequenced in a massively
parallel fashion in step 540. In step 550, all sequencing
information is analyzed, and based on the sequencing information
pertaining to the marker molecules; the integrity of the source
sample is verified in step 560. Verification of the integrity of
each of the plurality of source samples is accomplished by first
grouping sequence tags associated with identical index sequences to
associate the genomic and marker sequences and distinguish
sequences belonging to each of the libraries made from genomic
molecules of a plurality of samples. Analysis of the grouped marker
and genomic sequences is then performed to verify that the sequence
obtained for the marker molecules corresponds to the known unique
sequence added to the corresponding source sample. If the integrity
of the sample is verified, the sequencing information pertaining to
the genomic nucleic acids of the sample can be analyzed to provide
genetic information about the subject from which the source sample
was obtained. For example, if the integrity of the sample is
verified, the sequencing information pertaining to the genomic
nucleic acids is analyzed to determine the presence or absence of a
chromosomal abnormality. The absence of a correspondence between
the sequencing information and known sequence of the marker
molecule is indicative of a sample mix-up, and the accompanying
sequencing information pertaining to the genomic cfDNA molecules is
disregarded.
Determination of CNV for Prenatal Diagnoses
Cell-free fetal DNA and RNA circulating in maternal blood can be
used for the early non-invasive prenatal diagnosis (NIPD) of an
increasing number of genetic conditions, both for pregnancy
management and to aid reproductive decision-making. The presence of
cell-free DNA circulating in the bloodstream has been known for
over 50 years. More recently, presence of small amounts of
circulating fetal DNA was discovered in the maternal bloodstream
during pregnancy (Lo et al., Lancet 350:485-487 [1997]). Thought to
originate from dying placental cells, cell-free fetal DNA (cfDNA)
has been shown to consists of short fragments typically fewer than
200 bp in length Chan et al., Clin Chem 50:88-92 [2004]), which can
be discerned as early as 4 weeks gestation (Illanes et al., Early
Human Dev 83:563-566 [2007]), and known to be cleared from the
maternal circulation within hours of delivery (Lo et al., Am J Hum
Genet. 64:218-224 [1999]). In addition to cfDNA, fragments of
cell-free fetal RNA (cfRNA) can also be discerned in the maternal
bloodstream, originating from genes that are transcribed in the
fetus or placenta. The extraction and subsequent analysis of these
fetal genetic elements from a maternal blood sample offers novel
opportunities for NIPD.
The present method is a polymorphism-independent method that for
use in NIPD and that does not require that the fetal cfDNA be
distinguished from the maternal cfDNA to enable the determination
of a fetal aneuploidy. In some embodiments, the aneuploidy is a
complete chromosomal trisomy or monosomy, or a partial trisomy or
monosomy. Partial aneuploidies are caused by loss or gain of part
of a chromosome, and encompass chromosomal imbalances resulting
from unbalanced translocations, unbalanced inversions, deletions
and insertions. By far, the most common known aneuploidy compatible
with life is trisomy 21 i.e. Down Syndrome (DS), which is caused by
the presence of part or all of chromosome 21. Rarely, DS can be
caused by an inherited or sporadic defect whereby an extra copy of
all or part of chromosome 21 becomes attached to another chromosome
(usually chromosome 14) to form a single aberrant chromosome. DS is
associated with intellectual impairment, severe learning
difficulties and excess mortality caused by long-term health
problems such as heart disease. Other aneuploidies with known
clinical significance include Edward syndrome (trisomy 18) and
Patau Syndrome (trisomy 13), which are frequently fatal within the
first few months of life. Abnormalities associated with the number
of sex chromosomes are also known and include monosomy X e.g.
Turner syndrome (XO), and triple X syndrome (XXX) in female births
and Kleinefelter syndrome (XXY) and XYY syndrome in male births,
which are all associated with various phenotypes including
sterility and reduction in intellectual skills. Monosomy X [45,X]
is a common cause of early pregnancy loss accounting for about 7%
of spontaneous abortions. Based on the liveborn frequency of 45,X
(also called Turner syndrome) of 1-2/10,000, it is estimated that
less than 1% of 45,X conceptuses will survive to term. About 30% of
Turners syndrome patients are mosaic with both a 45,X cell line and
either a 46,XX cell line or one containing a rearranged X
chromosome (Hook and Warburton 1983). The phenotype in a liveborn
infant is relatively mild considering the high embryonic lethality
and it has been hypothesized that possibly all liveborn females
with Turner syndrome carry a cell line containing two sex
chromosomes. Monosomy X can occur in females as 45,X or as
45,X/46XX, and in males as 45,X/46XY. Autosomal monosomies in human
are generally suggested to be incompatible with life; however,
there is quite a number of cytogenetic reports describing full
monosomy of one chromosome 21 in live born children (Vosranova I et
al., Molecular Cytogen. 1:13 [2008]; Joosten et al., Prenatal
Diagn. 17:271-5 [1997]. The method described herein can be used to
diagnose these and other chromosomal abnormalities prenatally.
According to some embodiments the methods disclosed herein can
determine the presence or absence of chromosomal trisomies of any
one of chromosomes 1-22, X and Y. Examples of chromosomal trisomies
that can be detected according to the present method include
without limitation trisomy 21 (T21; Down Syndrome), trisomy 18
(T18; Edward's Syndrome), trisomy 16 (T16), trisomy 20 (T20),
trisomy 22 (T22; Cat Eye Syndrome), trisomy 15 (T15; Prader Willi
Syndrome), trisomy 13 (T13; Patau Syndrome), trisomy 8 (T8; Warkany
Syndrome), trisomy 9, and the XXY (Kleinefelter Syndrome), XYY, or
XXX trisomies. Complete trisomies of other autosomes existing in a
non-mosaic state are lethal, but can be compatible with life when
present in a mosaic state. It will be appreciated that various
complete trisomies, whether existing in a mosaic or non-mosaic
state, and partial trisomies can be determined in fetal cfDNA
according to the teachings provided herein.
Non-limiting examples of partial trisomies that can be determined
by the present method include, but are not limited to, partial
trisomy 1q32-44, trisomy 9 p, trisomy 4 mosaicism, trisomy 17p,
partial trisomy 4q26-qter, partial 2p trisomy, partial trisomy 1q,
and/or partial trisomy 6p/monosomy 6q.
The methods disclosed herein can be also used to determine
chromosomal monosomy X, chromosomal monosomy 21, and partial
monosomies such as, monosomy 13, monosomy 15, monosomy 16, monosomy
21, and monosomy 22, which are known to be involved in pregnancy
miscarriage. Partial monosomy of chromosomes typically involved in
complete aneuploidy can also be determined by the method described
herein. Non-limiting examples of deletion syndromes that can be
determined according to the present method include syndromes caused
by partial deletions of chromosomes. Examples of partial deletions
that can be determined according to the methods described herein
include without limitation partial deletions of chromosomes 1, 4,
5, 7, 11, 18, 15, 13, 17, 22 and 10, which are described in the
following.
1q21.1 deletion syndrome or 1q21.1 (recurrent) microdeletion is a
rare aberration of chromosome 1. Next to the deletion syndrome,
there is also a 1q21.1 duplication syndrome. While there is a part
of the DNA missing with the deletion syndrome on a particular spot,
there are two or three copies of a similar part of the DNA on the
same spot with the duplication syndrome. Literature refers to both
the deletion and the duplication as the 1q21.1 copy-number
variations (CNV). The 1q21.1 deletion can be associated with the
TAR Syndrome (Thrombocytopenia with Absent radius).
Wolf-Hirschhorn syndrome (WHS) (OMIN #194190) is a contiguous gene
deletion syndrome associated with a hemizygous deletion of
chromosome 4p16.3. Wolf-Hirschhorn syndrome is a congenital
malformation syndrome characterized by pre- and postnatal growth
deficiency, developmental disability of variable degree,
characteristic craniofacial features (`Greek warrior helmet`
appearance of the nose, high forehead, prominent glabella,
hypertelorism, high-arched eyebrows, protruding eyes, epicanthal
folds, short philtrum, distinct mouth with downturned corners, and
micrognathia), and a seizure disorder.
Partial deletion of chromosome 5, also known as 5p- or 5p minus,
and named Cris du Chat syndrome (OMIN#123450), is caused by a
deletion of the short arm (p arm) of chromosome 5 (5p15.3-p15.2).
Infants with this condition often have a high-pitched cry that
sounds like that of a cat. The disorder is characterized by
intellectual disability and delayed development, small head size
(microcephaly), low birth weight, and weak muscle tone (hypotonia)
in infancy, distinctive facial features and possibly heart
defects.
Williams-Beuren Syndrome also known as chromosome 7q11.23 deletion
syndrome (OMIN 194050) is a contiguous gene deletion syndrome
resulting in a multisystem disorder caused by hemizygous deletion
of 1.5 to 1.8 Mb on chromosome 7q11.23, which contains
approximately 28 genes.
Jacobsen Syndrome, also known as 11q deletion disorder, is a rare
congenital disorder resulting from deletion of a terminal region of
chromosome 11 that includes band 11q24.1. It can cause intellectual
disabilities, a distinctive facial appearance, and a variety of
physical problems including heart defects and a bleeding
disorder.
Partial monosomy of chromosome 18, known as monosomy 18p is a rare
chromosomal disorder in which all or part of the short arm (p) of
chromosome 18 is deleted (monosomic). The disorder is typically
characterized by short stature, variable degrees of mental
retardation, speech delays, malformations of the skull and facial
(craniofacial) region, and/or additional physical abnormalities.
Associated craniofacial defects may vary greatly in range and
severity from case to case.
Conditions caused by changes in the structure or number of copies
of chromosome 15 include Angelman Syndrome and Prader-Willi
Syndrome, which involve a loss of gene activity in the same part of
chromosome 15, the 15q11-q13 region. It will be appreciated that
several translocations and microdeletions can be asymptomatic in
the carrier parent, yet can cause a major genetic disease in the
offspring. For example, a healthy mother who carries the 15q11-q13
microdeletion can give birth to a child with Angelman syndrome, a
severe neurodegenerative disorder. Thus, the methods, apparatus and
systems described herein can be used to identify such a partial
deletion and other deletions in the fetus.
Partial monosomy 13q is a rare chromosomal disorder that results
when a piece of the long arm (q) of chromosome 13 is missing
(monosomic). Infants born with partial monosomy 13q may exhibit low
birth weight, malformations of the head and face (craniofacial
region), skeletal abnormalities (especially of the hands and feet),
and other physical abnormalities. Mental retardation is
characteristic of this condition. The mortality rate during infancy
is high among individuals born with this disorder. Almost all cases
of partial monosomy 13q occur randomly for no apparent reason
(sporadic).
Smith-Magenis syndrome (SMS--OMIM #182290) is caused by a deletion,
or loss of genetic material, on one copy of chromosome 17. This
well-known syndrome is associated with developmental delay, mental
retardation, congenital anomalies such as heart and kidney defects,
and neurobehavioral abnormalities such as severe sleep disturbances
and self-injurious behavior. Smith-Magenis syndrome (SMS) is caused
in most cases (90%) by a 3.7-Mb interstitial deletion in chromosome
17p11.2.
22q11.2 deletion syndrome, also known as DiGeorge syndrome, is a
syndrome caused by the deletion of a small piece of chromosome 22.
The deletion (22 q11.2) occurs near the middle of the chromosome on
the long arm of one of the pair of chromosome. The features of this
syndrome vary widely, even among members of the same family, and
affect many parts of the body. Characteristic signs and symptoms
may include birth defects such as congenital heart disease, defects
in the palate, most commonly related to neuromuscular problems with
closure (velo-pharyngeal insufficiency), learning disabilities,
mild differences in facial features, and recurrent infections.
Microdeletions in chromosomal region 22q11.2 are associated with a
20 to 30-fold increased risk of schizophrenia.
Deletions on the short arm of chromosome 10 are associated with a
DiGeorge Syndrome like phenotype. Partial monosomy of chromosome
10p is rare but has been observed in a portion of patients showing
features of the DiGeorge Syndrome.
In one embodiment, the methods, apparatus, and systems described
herein is used to determine partial monosomies including but not
limited to partial monosomy of chromosomes 1, 4, 5, 7, 11, 18, 15,
13, 17, 22 and 10, e.g. partial monosomy 1q21.11, partial monosomy
4p16.3, partial monosomy 5p15.3-p15.2, partial monosomy 7q11.23,
partial monosomy 11q24.1, partial monosomy 18p, partial monosomy of
chromosome 15 (15q11-q13), partial monosomy 13q, partial monosomy
17p11.2, partial monosomy of chromosome 22 (22q11.2), and partial
monosomy 10p can also be determined using the method.
Other partial monosomies that can be determined according to the
methods described herein include unbalanced translocation t(8;
11)(p23.2;p15.5); 11q23 microdeletion; 17p11.2 deletion; 22q13.3
deletion; Xp22.3 microdeletion; 10p14 deletion; 20p microdeletion,
[del(22)(q11.2q11.23)], 7q11.23 and 7q36 deletions; 1p36 deletion;
2p microdeletion; neurofibromatosis type 1 (17q11.2 microdeletion),
Yq deletion; 4p16.3 microdeletion; 1p36.2 microdeletion; 11q14
deletion; 19q13.2 microdeletion; Rubinstein-Taybi (16 p13.3
microdeletion); 7p21 microdeletion; Miller-Dieker syndrome
(17p13.3); and 2q37 microdeletion. Partial deletions can be small
deletions of part of a chromosome, or they can be microdeletions of
a chromosome where the deletion of a single gene can occur.
Several duplication syndromes caused by the duplication of part of
chromosome arms have been identified (see OMIN [Online Mendelian
Inheritance in Man viewed online at ncbi.nlm.nih.gov/omim]). In one
embodiment, the present method can be used to determine the
presence or absence of duplications and/or multiplications of
segments of any one of chromosomes 1-22, X and Y. Non-limiting
examples of duplications syndromes that can be determined according
to the present method include duplications of part of chromosomes
8, 15, 12, and 17, which are described in the following.
8p23.1 duplication syndrome is a rare genetic disorder caused by a
duplication of a region from human chromosome 8. This duplication
syndrome has an estimated prevalence of 1 in 64,000 births and is
the reciprocal of the 8p23.1 deletion syndrome. The 8p23.1
duplication is associated with a variable phenotype including one
or more of speech delay, developmental delay, mild dysmorphism,
with prominent forehead and arched eyebrows, and congenital heart
disease (CHD).
Chromosome 15q Duplication Syndrome (Dup15q) is a clinically
identifiable syndrome which results from duplications of chromosome
15q11-13.1 Babies with Dup15q usually have hypotonia (poor muscle
tone), growth retardation; they may be born with a cleft lip and/or
palate or malformations of the heart, kidneys or other organs; they
show some degree of cognitive delay/disability (mental
retardation), speech and language delays, and sensory processing
disorders.
Pallister Killian syndrome is a result of extra #12 chromosome
material. There is usually a mixture of cells (mosaicism), some
with extra #12 material, and some that are normal (46 chromosomes
without the extra #12 material). Babies with this syndrome have
many problems including severe mental retardation, poor muscle
tone, "coarse" facial features, and a prominent forehead. They tend
to have a very thin upper lip with a thicker lower lip and a short
nose. Other health problems include seizures, poor feeding, stiff
joints, cataracts in adulthood, hearing loss, and heart defects.
Persons with Pallister Killian have a shortened lifespan.
Individuals with the genetic condition designated as
dup(17)(p11.2p11.2) or dup 17p carry extra genetic information
(known as a duplication) on the short arm of chromosome 17.
Duplication of chromosome 17p11.2 underlies Potocki-Lupski syndrome
(PTLS), which is a newly recognized genetic condition with only a
few dozen cases reported in the medical literature. Patients who
have this duplication often have low muscle tone, poor feeding, and
failure to thrive during infancy, and also present with delayed
development of motor and verbal milestones. Many individuals who
have PTLS have difficulty with articulation and language
processing. In addition, patients may have behavioral
characteristics similar to those seen in persons with autism or
autism-spectrum disorders. Individuals with PTLS may have heart
defects and sleep apnea. A duplication of a large region in
chromosome 17p12 that includes the gene PMP22 is known to cause
Charcot-Marie Tooth disease.
CNV have been associated with stillbirths. However, due to inherent
limitations of conventional cytogenetics, the contribution of CNV
to stillbirth is thought to be underrepresented (Harris et al.,
Prenatal Diagn 31:932-944 [2011]). As is shown in the examples and
described elsewhere herein, the present method is capable of
determining the presence of partial aneuploidies e.g. deletions and
multiplications of chromosome segments, and can be used to identify
and determine the presence or absence of CNV that are associated
with stillbirths.
Determination of Complete Fetal Chromosomal Aneuploidies
In one embodiment, methods are provided for determining the
presence or absence of any one or more different complete fetal
chromosomal aneuploidies in a maternal test sample comprising fetal
and maternal nucleic acid molecules. Preferably, the method
determines the presence or absence of any four or more different
complete chromosomal aneuploidies. The steps of the method comprise
(a) obtaining sequence information for the fetal and maternal
nucleic acids in the maternal test sample; and (b) using the
sequence information to identify a number of sequence tags for each
of any one or more chromosomes of interest selected from
chromosomes 1-22, X and Y and to identify a number of sequence tags
for a normalizing chromosome sequence for each of the any one or
more chromosomes of interest. The normalizing chromosome sequence
can be a single chromosome, or it can be a group of chromosomes
selected from chromosomes 1-22, X, and Y. The method further uses
in step (c) the number of sequence tags identified for each of the
any one or more chromosomes of interest and the number of sequence
tags identified for each normalizing chromosome sequence to
calculate a single chromosome dose for each of the any one or more
chromosomes of interest; and (d) compares each of the single
chromosome doses for each of the any one or more chromosomes of
interest to a threshold value for each of the one or more
chromosomes of interest, thereby determining the presence or
absence of any one or more complete different fetal chromosomal
aneuploidies in the maternal test sample.
In some embodiments, step (c) comprises calculating a single
chromosome dose for each chromosomes of interest as the ratio of
the number of sequence tags identified for each of the chromosomes
of interest and the number of sequence tags identified for the
normalizing chromosome for each of the chromosomes of interest.
In other embodiments, step (c) comprises calculating a single
chromosome dose for each of the chromosomes of interest as the
ratio of the number of sequence tags identified for each of the
chromosomes of interest and the number of sequence tags identified
for the normalizing chromosome for each of the chromosomes of
interest. In other embodiments, step (c) comprises calculating a
sequence tag ratio for a chromosome of interest by relating the
number of sequence tags obtained for the chromosome of interest to
the length of the chromosome of interest, and relating the number
of tags for the corresponding normalizing chromosome sequence for
the chromosome of interest to the length of the normalizing
chromosome sequence, and calculating a chromosome dose for the
chromosome of interest as a ratio of the sequence tags density of
the chromosome of interest and the sequence tag density for the
normalizing sequence. The calculation is repeated for each of all
chromosomes of interest. Steps (a)-(d) can be repeated for test
samples from different maternal subjects.
An example of the embodiment whereby four or more complete fetal
chromosomal aneuploidies are determined in a maternal test sample
comprising a mixture of fetal and maternal cell-free DNA molecules,
comprises: (a) sequencing at least a portion of cell-free DNA
molecules to obtain sequence information for the fetal and maternal
cell-free DNA molecules in the test sample; (b) using the sequence
information to identify a number of sequence tags for each of any
twenty or more chromosomes of interest selected from chromosomes
1-22, X, and Y and to identify a number of sequence tags for a
normalizing chromosome for each of the twenty or more chromosomes
of interest; (c) using the number of sequence tags identified for
each of the twenty or more chromosomes of interest and the number
of sequence tags identified for each the normalizing chromosome to
calculate a single chromosome dose for each of the twenty or more
chromosomes of interest; and (d) comparing each of the single
chromosome doses for each of the twenty or more chromosomes of
interest to a threshold value for each of the twenty or more
chromosomes of interest, and thereby determining the presence or
absence of any twenty or more different complete fetal chromosomal
aneuploidies in the test sample.
In another embodiment, the method for determining the presence or
absence of any one or more different complete fetal chromosomal
aneuploidies in a maternal test sample as described above uses a
normalizing segment sequence for determining the dose of the
chromosome of interest. In this instance, the method comprises (a)
obtaining sequence information for said fetal and maternal nucleic
acids in said sample; (b) using said sequence information to
identify a number of sequence tags for each of any one or more
chromosomes of interest selected from chromosomes 1-22, X and Y and
to identify a number of sequence tags for a normalizing segment
sequence for each of said any one or more chromosomes of interest.
The normalizing segment sequence can be a single segment of a
chromosome or it can be a group of segments form one or more
different chromosomes. The method further uses in step (c) the
number of sequence tags identified for each of said any one or more
chromosomes of interest and said number of sequence tags identified
for said normalizing segment sequence to calculate a single
chromosome dose for each of said any one or more chromosomes of
interest; and (d) comparing each of said single chromosome doses
for each of said any one or more chromosomes of interest to a
threshold value for each of said one or more chromosomes of
interest, and thereby determining the presence or absence of one or
more different complete fetal chromosomal aneuploidies in said
sample.
In some embodiments, step (c) comprises calculating a single
chromosome dose for each of said chromosomes of interest as the
ratio of the number of sequence tags identified for each of said
chromosomes of interest and the number of sequence tags identified
for said normalizing segment sequence for each of said chromosomes
of interest.
In other embodiments, step (c) comprises calculating a sequence tag
ratio for a chromosome of interest by relating the number of
sequence tags obtained for the chromosome of interest to the length
of the chromosome of interest, and relating the number of tags for
the corresponding normalizing segment sequence for the chromosome
of interest to the length of the normalizing segment sequence, and
calculating a chromosome dose for the chromosome of interest as a
ratio of the sequence tags density of the chromosome of interest
and the sequence tag density for the normalizing segment sequence.
The calculation is repeated for each of all chromosomes of
interest. Steps (a)-(d) can be repeated for test samples from
different maternal subjects.
A means for comparing chromosome doses of different sample sets is
provided by determining a normalized chromosome value (NCV), which
relates the chromosome dose in a test sample to the mean of the of
the corresponding chromosome dose in a set of qualified samples.
The NCV is calculated as:
.mu..sigma. ##EQU00006## where {circumflex over (.mu.)}.sub.j and
{circumflex over (.sigma.)}.sub.j the estimated mean and standard
deviation, respectively, for the j-th chromosome dose in a set of
qualified samples, and x.sub.ij is the observed j-th chromosome
dose for test sample i.
In some embodiments, the presence or absence of at least one
complete fetal chromosomal aneuploidy is determined. In other
embodiments, the presence or absence of at least two, at least
three, at least four, at least five, at least six, at least seven,
at least eight, at least nine, at least ten, at least eleven, at
least twelve, at least thirteen, at least fourteen, at least
fifteen, at least sixteen, at least seventeen, at least eighteen,
at least nineteen, at least twenty, at least twenty-one, at least
twenty-two, at least twenty-three, or twenty-four complete fetal
chromosomal aneuploidies are determined in a sample, wherein
twenty-two of the complete fetal chromosomal aneuploidies
correspond to complete chromosomal aneuploidies of any one or more
of the autosomes; the twenty-third and twenty fourth chromosomal
aneuploidy correspond to a complete fetal chromosomal aneuploidy of
chromosomes X and Y. As aneuploidies of sex chromosomes can
comprise tetrasomies, pentasomies and other polysomies, the number
of different complete chromosomal aneuploidies that can be
determined according to the present method may be at least 24, at
least 25, at least 26, at least 27, at least 28, at least 29, or at
least 30 complete chromosomal aneuploidies. Thus, the number of
different complete fetal chromosomal aneuploidies that are
determined is related to the number of chromosomes of interest that
are selected for analysis.
In one embodiment, determining the presence or absence of any one
or more different complete fetal chromosomal aneuploidies in a
maternal test sample as described above uses a normalizing segment
sequence for one chromosome of interest, which is selected from
chromosomes 1-22, X, and Y. In other embodiments, two or more
chromosomes of interest are selected from any two or more of
chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, X, or Y. In one embodiment, any one or more
chromosomes of interest are selected from chromosomes 1-22, X, and
Y comprise at least twenty chromosomes selected from chromosomes
1-22, X, and Y, and wherein the presence or absence of at least
twenty different complete fetal chromosomal aneuploidies is
determined. In other embodiments, any one or more chromosomes of
interest selected from chromosomes 1-22, X, and Y is all of
chromosomes 1-22, X, and Y, and wherein the presence or absence of
complete fetal chromosomal aneuploidies of all of chromosomes 1-22,
X, and Y is determined. Complete different fetal chromosomal
aneuploidies that can be determined include complete chromosomal
trisomies, complete chromosomal monosomies and complete chromosomal
polysomies. Examples of complete fetal chromosomal aneuploidies
include without limitation trisomies of any one or more of the
autosomes e.g. trisomy 2, trisomy 8, trisomy 9, trisomy 20, trisomy
21, trisomy 13, trisomy 16, trisomy 18, trisomy 22; trisomies of
the sex chromosomes e.g. 47,XXY, 47 XXX, and 47 XYY; tetrasomies of
sex chromosomes e.g. 48,XXYY, 48,XXXY, 48XXXX, and 48,XYYY;
pentasomies of sex chromosomes e.g. 49,XXXYY 49,XXXXY, 49,XXXXX,
49,XYYYY; and monosomy X. Other complete fetal chromosomal
aneuploidies that can be determined according to the present method
are described below.
Determination of Partial Fetal Chromosomal Aneuploidies
In another embodiment, method are provided for determining the
presence or absence of any one or more different partial fetal
chromosomal aneuploidies in a maternal test sample comprising fetal
and maternal nucleic acid molecules. The steps of the method
comprise (a) obtaining sequence information for the fetal and
maternal nucleic acids in said sample; and (b) using the sequence
information to identify a number of sequence tags for each of any
one or more segments of any one or more chromosomes of interest
selected from chromosomes 1-22, X, and Y and to identify a number
of sequence tags for a normalizing segment sequence for each of
said any one or more segments of any one or more chromosomes of
interest. The normalizing segment sequence can be a single segment
of a chromosome or it can be a group of segments form one or more
different chromosomes. The method further uses in step (c) the
number of sequence tags identified for each of any one or more
segments of any one or more chromosomes of interest and the number
of sequence tags identified for the normalizing segment sequence to
calculate a single segment dose for each of any one or more
segments of any one or more chromosome of interest; and (d)
comparing each of the single chromosome doses for each of any one
or more segments of any one or more chromosomes of interest to a
threshold value for each of said any one or more chromosomal
segments of any one or more chromosome of interest, and thereby
determining the presence or absence of one or more different
partial fetal chromosomal aneuploidies in said sample.
In some embodiments, step (c) comprises calculating a single
segment dose for each of any one or more segments of any one or
more chromosomes of interest as the ratio of the number of sequence
tags identified for each of any one or more segments of any one or
more chromosomes of interest and the number of sequence tags
identified for the normalizing segment sequence for each of any one
or more segments of any one or more chromosomes of interest.
In other embodiments, step (c) comprises calculating a sequence tag
ratio for a segment of interest by relating the number of sequence
tags obtained for the segment of interest to the length of the
segment of interest, and relating the number of tags for the
corresponding normalizing segment sequence for the segment of
interest to the length of the normalizing segment sequence, and
calculating a segment dose for the segment of interest as a ratio
of the sequence tags density of the segment of interest and the
sequence tag density for the normalizing segment sequence. The
calculation is repeated for each of all chromosomes of interest.
Steps (a)-(d) can be repeated for test samples from different
maternal subjects.
A means for comparing segment doses of different sample sets is
provided by determining a normalized segment value (NSV), which
relates the segment dose in a test sample to the mean of the of the
corresponding segment dose in a set of qualified samples. The NSV
is calculated as:
.mu..sigma. ##EQU00007## where {circumflex over (.mu.)}.sub.j and
{circumflex over (.sigma.)}.sub.j are the estimated mean and
standard deviation, respectively, for the j-th segment dose in a
set of qualified samples, and x.sub.ij is the observed j-th segment
dose for test sample i.
In some embodiments, the presence or absence of one partial fetal
chromosomal aneuploidy is determined. In other embodiments, the
presence or absence of two, three, four, five, six, seven, eight,
nine, ten, fifteen, twenty, twenty-five, or more partial fetal
chromosomal aneuplodies are determined in a sample. In one
embodiment, one segment of interest selected from any one of
chromosomes 1-22, X, and Y is selected from chromosomes 1-22, X,
and Y. In another embodiment, two or more segments of interest
selected from chromosomes 1-22, X, and Y are selected from any two
or more of chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, X, or Y. In one embodiment, any
one or more segments of interest are selected from chromosomes
1-22, X, and Y comprise at least one, five, ten, 15, 20, 25 or more
segments selected from chromosomes 1-22, X, and Y, and wherein the
presence or absence of at least one, five, ten, 15, 20, 25
different partial fetal chromosomal aneuploidies is determined.
Different partial fetal chromosomal aneuploidies that can be
determined include fetal chromosomal aneuploidies include partial
duplications, partial multiplications, partial insertions and
partial deletions. Examples of partial fetal chromosomal
aneuploidies include partial monosomies and partial trisomies of
autosomes. Partial monosomies of autosomes include partial monosomy
of chromosome 1, partial monosomy of chromosome 4, partial monosomy
of chromosome 5, partial monosomy of chromosome 7, partial monosomy
of chromosome 11, partial monosomy of chromosome 15, partial
monosomy of chromosome 17, partial monosomy of chromosome 18, and
partial monosomy of chromosome 22. Other partial fetal chromosomal
aneuploidies that can be determined according to the present method
are described below.
In any one of the embodiments described above, the test sample is a
maternal sample selected from blood, plasma, serum, urine and
saliva samples. In some embodiments, the maternal test sample is a
plasma sample. The nucleic acid molecules of the maternal sample
are a mixture of fetal and maternal cell-free DNA molecules.
Sequencing of the nucleic acids can be performed using next
generation sequencing (NGS) as described elsewhere herein. In some
embodiments, sequencing is massively parallel sequencing using
sequencing-by-synthesis with reversible dye terminators. In other
embodiments, sequencing is sequencing-by-ligation. In yet other
embodiments, sequencing is single molecule sequencing. Optionally,
an amplification step is performed prior to sequencing.
Determination of CNV of Clinical Disorders
In addition to the early determination of birth defects, the
methods described herein can be applied to the determination of any
abnormality in the representation of genetic sequences within the
genome. A number of abnormalities in the representation of genetic
sequences within the genome have been associated with various
pathologies. Such pathologies include, but are not limited to
cancer, infectious and autoimmune diseases, diseases of the nervous
system, metabolic and/or cardiovascular diseases, and the like.
Accordingly in various embodiments use of the methods described
herein in the diagnosis, and/or monitoring, and or treating such
pathologies is contemplated. For example, the methods can be
applied to determining the presence or absence of a disease, to
monitoring the progression of a disease and/or the efficacy of a
treatment regimen, to determining the presence or absence of
nucleic acids of a pathogen e.g. virus; to determining chromosomal
abnormalities associated with graft versus host disease (GVHD), and
to determining the contribution of individuals in forensic
analyses.
CNVs in Cancer
It has been shown that blood plasma and serum DNA from cancer
patients contains measurable quantities of tumor DNA, that can be
recovered and used as surrogate source of tumor DNA, and tumors are
characterized by aneuploidy, or inappropriate numbers of gene
sequences or even entire chromosomes. The determination of a
difference in the amount of a given sequence i.e. a sequence of
interest, in a sample from an individual can thus be used in the
prognosis or diagnosis of a medical condition. In some embodiments,
the present method can be used to determine the presence or absence
of a chromosomal aneuploidy in a patient suspected or known to be
suffering from cancer.
In certain embodiments the aneuploidy is characteristic of the
genome of the subject and results in a generally increased
predisposition to a cancer. In certain embodiments the aneuploidy
is characteristic of particular cells (e.g., tumor cells,
proto-tumor neoplastic cells, etc.) that are or have an increased
predisposition to neoplasia. Particular aneuploidies are associated
with particular cancers or predispositions to particular cancers as
described below.
Accordingly, various embodiments of the methods described herein
provide a determination of copy number variation of sequence(s) of
interest e.g. clinically-relevant sequence(s), in a test sample
from a subject where certain variations in copy number provide an
indicator of the presence and/or a predisposition to a cancer. In
certain embodiments the sample comprises a mixture of nucleic acids
is derived from two or more types of cells. In one embodiment, the
mixture of nucleic acids is derived from normal and cancerous cells
derived from a subject suffering from a medical condition e.g.
cancer.
The development of cancer is often accompanied by an alteration in
number of whole chromosomes i.e. complete chromosomal aneuploidy,
and/or an alteration in the number of segments of chromosomes i.e.
partial aneuploidy, caused by a process known as chromosome
instability (CIN) (Thoma et al., Swiss Med Weekly 2011:141:w13170).
It is believed that many solid tumors, such as breast cancer,
progress from initiation to metastasis through the accumulation of
several genetic aberrations. [Sato et al., Cancer Res., 50:
7184-7189 [1990]; Jongsma et al., J Clin Pathol: Mol Path
55:305-309 [2002])]. Such genetic aberrations, as they accumulate,
may confer proliferative advantages, genetic instability and the
attendant ability to evolve drug resistance rapidly, and enhanced
angiogenesis, proteolysis and metastasis. The genetic aberrations
may affect either recessive "tumor suppressor genes" or dominantly
acting oncogenes. Deletions and recombination leading to loss of
heterozygosity (LOH) are believed to play a major role in tumor
progression by uncovering mutated tumor suppressor alleles.
cfDNA has been found in the circulation of patients diagnosed with
malignancies including but not limited to lung cancer (Pathak et
al. Clin Chem 52:1833-1842 [2006]), prostate cancer (Schwartzenbach
et al. Clin Cancer Res 15:1032-8 [2009]), and breast cancer
(Schwartzenbach et al. available online at
breast-cancer-research.com/content/11/5/R71 [2009]). Identification
of genomic instabilities associated with cancers that can be
determined in the circulating cfDNA in cancer patients is a
potential diagnostic and prognostic tool. In one embodiment,
methods described herein are used to determine CNV of one or more
sequence(s) of interest in a sample, e.g., a sample comprising a
mixture of nucleic acids derived from a subject that is suspected
or is known to have cancer e.g. carcinoma, sarcoma, lymphoma,
leukemia, germ cell tumors and blastoma. In one embodiment, the
sample is a plasma sample derived (processed) from peripheral blood
that may comprise a mixture of cfDNA derived from normal and
cancerous cells. In another embodiment, the biological sample that
is needed to determine whether a CNV is present is derived from a
cells that, if a cancer is present, comprise a mixture of cancerous
and non-cancerous cells from other biological tissues including,
but not limited to biological fluids such as serum, sweat, tears,
sputum, urine, sputum, ear flow, lymph, saliva, cerebrospinal
fluid, ravages, bone marrow suspension, vaginal flow, transcervical
lavage, brain fluid, ascites, milk, secretions of the respiratory,
intestinal and genitourinary tracts, and leukophoresis samples, or
in tissue biopsies, swabs, or smears. In other embodiments, the
biological sample is a stool (fecal) sample.
The methods described herein are not limited to the analysis of
cfDNA. It will be recognized that similar analyses can be performed
on cellular DNA samples.
In various embodiments the sequence(s) of interest comprise nucleic
acid sequence(s) known or is suspected to play a role in the
development and/or progression of the cancer. Examples of a
sequence of interest include nucleic acids sequences e.g. complete
chromosomes and/or segments of chromosomes, that are amplified or
deleted in cancerous cells as described below.
Total CNV Number and Risk for Cancer.
Common cancer SNPs--and by analogy common cancer CNVs may each
confer only a minor increase in disease risk. However, collectively
they may cause a substantially elevated risk for cancers. In this
regard it is noted that germline gains and losses of large DNA
segments have been reported as factors predisposing individuals to
neuroblastoma, prostate and colorectal cancer, breast cancer, and
BRCA1-associated ovarian cancer (see, e.g., Krepischi et al. Breast
Cancer Res., 14: R24 [2012]; Diskin et al. Nature 2009,
459:987-991; Liu et al. Cancer Res 2009, 69: 2176-2179; Lucito et
al. Cancer Biol Ther 2007, 6:1592-1599; Thean et al. Genes
Chromosomes Cancer 2010, 49:99-106; Venkatachalam et al. Int J
Cancer 2011, 129:1635-1642; and Yoshihara et al. Genes Chromosomes
Cancer 2011, 50:167-177). It is noted that CNVs frequently found in
the healthy population (common CNVs) are believed to have a role in
cancer etiology (see, e.g., Shlien and Malkin (2009) Genome
Medicine, 1(6): 62). In one study testing the hypothesis that
common CNVs are associated with malignancy (Shlien et al. Proc Natl
Acad Sci USA 2008, 105:11264-11269) a map of every known CNV whose
locus coincides with that of bona fide cancer-related genes (as
catalogued by Higgins et al. Nucleic Acids Res 2007, 35:D721-726)
was created. These were termed "cancer CNVs". In an initial
analysis (Shlien et al. Proc Natl Acad Sci USA 2008,
105:11264-11269), 770 healthy genomes were evaluated using the
Affymetrix 500K array set, which has an average inter-probe
distance of 5.8 kb. As CNVs are generally thought to be depleted in
gene regions (Redon et al. (2006) Nature 2006, 444:444-454), it was
surprising to find 49 cancer genes that were directly encompassed
or overlapped by a CNV in more than one person in a large reference
population. In the top ten genes, cancer CNVs could be found in
four or more people.
It is thus believed that CNV frequency can be used as a measure of
risk for cancer (see, e.g., U.S. Patent Publication No:
2010/0261183 A1). The CNV frequency can be determined simply by the
constitutive genome of the organism or it can represent a fraction
derived from one or more tumors (neoplastic cells) if such are
present.
In certain embodiments a number of CNVs in a test sample (e.g., a
sample comprising a constitutional (germline) nucleic acid) or a
mixture of nucleic acids (e.g., a germline nucleic acid and nucleic
acid(s) derived from neoplastic cells) is determined using the
methods described herein for copy number variations. Identification
of an increased number of CNVs in the test sample, e.g., in
comparison to a reference value is indicative of a risk of or
predisposition for cancer in the subject. It will be appreciated
that the reference value may vary with a given population. It will
also be appreciated that the absolute value of the increase in CNV
frequency will vary depending on the resolution of the method
utilized to determine CNV frequency and other parameters.
Typically, an increase in CNV frequency of at least about 1.2 times
the reference value been determined to indicative of risk for
cancer (see, e.g., U.S. Patent Publication No: 2010/0261183 A1),
for example an increase in CNV frequency of at least or about 1.5
times the reference value or greater, such as 2-4 times the
reference value is an indicator of an increased risk of cancer
(e.g., as compared to the normal healthy reference population).
A determination of structural variation in the genome of a mammal
in comparison to a reference value is also believed to be
indicative of risk of cancer. In this context, in one embodiment,
the term "structural variation" is can be defined as the CNV
frequency in a mammal multiplied by the average CNV size (in bp) in
the mammal. Thus, high structural variation scores will result due
to increased CNV frequency and/or due to the occurrence of large
genomic nucleic acid deletions or duplications. Accordingly, in
certain embodiments a number of CNVs in a test sample (e.g., a
sample comprising a constitutional (germline) nucleic acid) is
determined using the methods described herein to determine size and
number of copy number variations. In certain embodiments a total
structural variation score within genomic DNA of greater than about
1 megabase, or greater than about 1.1 megabases, or greater than
about 1.2 megabases, or greater than about 1.3 megabases, or
greater than about 1.4 megabases, or greater than about 1.5
megabases, or greater than about 1.8 megabases, or greater than
about 2 megabases of DNA is indicative of risk of cancer.
It is believed these methods provide a measure of the risk of any
cancer including but not limited to, acute and chronic leukemias,
lymphomas, numerous solid tumors of mesenchymal or epithelial
tissue, brain, breast, liver, stomach, colon cancer, B cell
lymphoma, lung cancer, a bronchus cancer, a colorectal cancer, a
prostate cancer, a breast cancer, a pancreas cancer, a stomach
cancer, an ovarian cancer, a urinary bladder cancer, a brain or
central nervous system cancer, a peripheral nervous system cancer,
an esophageal cancer, a cervical cancer, a melanoma, a uterine or
endometrial cancer, a cancer of the oral cavity or pharynx, a liver
cancer, a kidney cancer, a biliary tract cancer, a small bowel or
appendix cancer, a salivary gland cancer, a thyroid gland cancer, a
adrenal gland cancer, an osteosarcoma, a chondrosarcoma, a
liposarcoma, a testes cancer, and a malignant fibrous histiocytoma,
and other cancers.
Full Chromosome Aneuploidies.
As indicated above, there exists a high frequency of aneuploidy in
cancer. In certain studies examining the prevalence of somatic copy
number alterations (SCNAs) in cancer, it has been discovered that
one-quarter of the genome of a typical cancer cell is affected
either by whole-arm SCNAs or by the whole-chromosome SCNAs of
aneuploidy (see, e.g., Beroukhim et al. Nature 463: 899-905
[2010]). Whole-chromosome alterations are recurrently observed in
several cancer types. For example, the gain of chromosome 8 is seen
in 10-20% of cases of acute myeloid leukaemia (AML), as well as
some solid tumours, including Ewing's Sarcoma and desmoid tumours
(see, e.g., Barnard et al. Leukemia 10: 5-12 [1996]; Maurici et al.
Cancer Genet. Cytogenet. 100: 106-110 [1998]; Qi et al. Cancer
Genet. Cytogenet. 92: 147-149 [1996]; Barnard, D. R. et al. Blood
100: 427-434 [2002]; and the like. Illustrative, but non-limiting
list of chromosome gains and losses in human cancers are shown in
Table 1.
TABLE-US-00001 TABLE 1 Illustrative specific, recurrent chromosome
gains and losses in human cancer (see, e.g., Gordon et al. (2012)
Nature Rev. Genetics, 13: 189-203). Gains Losses Chromosome Cancer
Type Cancer Type 1 Multiple Adenocarcinoma myeloma (kidney)
Adenocarcinoma (breast) 2 Hepatoblastoma Ewing`s sarcoma 3 Multiple
myeloma Melanoma Diffuse large Adenocarcinoma B-cell lymphoma
(kidney) 4 Acute lymphoblastic Adenocarcinoma leukaemia (kidney) 5
Multiple myeloma Adenocarcinoma (kidney) 6 Acute lymphoblastic
Adenocarcinoma leukaemia (kidney) Wilms` tumour 7 Adenocarcinoma
Acute myeloid (kidney) leukaemia Adenocarcinoma Juvenile
myelomonocytic (intestine) leukaemia 8 Acute myeloid Adenocarcinoma
leukaemia (kidney) Chronic myeloid leukaemia Ewing`s sarcoma 9
Multiple myeloma Polycythaemia vera 10 Acute lymphoblastic
Astrocytoma leukaemia Adenocarcinoma Multiple (uterus) myeloma 11
Multiple myeloma 12 Chronic lymphocytic Multiple leukaemia myeloma
Wilms` tumor 13 Acute myeloid Multiple leukaemia myeloma Wilms`
tumor 14 Acute lymphoblastic Adenocarcinoma leukaemia (kidney)
Meningioma 15 Multiple myeloma 16 Adenocarcinoma Multiple myeloma
(kidney) 17 Adenocarcinoma (kidney) Acute lymphoblastic leukaemia
18 Acute lymphoblastic Adenocarcinoma leukaemia (kidney) Wilms`
tumour 19 Multiple myeloma Adenocarcinoma Chronic myeloid (Breast)
leukaemia Meningioma 20 Hepatoblastoma Adenocarcinoma (kidney) 21
Acute lymphoblastic leukaemia Acute megakaryoblastic leukaemia 22
Acute lymphoblastic Meningioma leukaemia X Acute lymphoblastic
leukaemia Follicular lymphoma Y
In various embodiments, the methods described herein can be used to
detect and/or quantify whole chromosome aneuploidies that are
associated with cancer generally, and/or that are associated with
particular cancers. Thus, for example, in certain embodiments,
detection and/or quantification of whole chromosome aneuploidies
characterized by the gains or losses shown in Table 1 are
contemplated.
Arm Level Chromosomal Segment Copy Number Variations.
Multiple studies have reported patterns of arm-level copy number
variations across large numbers of cancer specimens (Lin et al.
Cancer Res 68, 664-673 (2008); George et al. PLoS ONE 2, e255
(2007); Demichelis et al. Genes Chromosomes Cancer 48: 366-380
(2009); Beroukhim et al. Nature. 463(7283): 899-905 [2010]). It has
additionally been observed that the frequency of arm-level copy
number variations decreases with the length of chromosome arms.
Adjusted for this trend, the majority of chromosome arms exhibit
strong evidence of preferential gain or loss, but rarely both,
across multiple cancer lineages (see, e.g., Beroukhim et al.
Nature. 463(7283): 899-905 [2010]).
Accordingly, in one embodiment, methods described herein are used
to determine arm level CNVs (CNVs comprising one chromosomal arm or
substantially one chromosomal arm) in a sample. The CNVs can be
determined in a CNVs in a test sample comprising a constitutional
(germline) nucleic acid and the arm level CNVs can be identified in
those constitutional nucleic acids. In certain embodiments arm
level CNVs are identified (if present) in a sample comprising a
mixture of nucleic acids (e.g., nucleic acids derived from normal
and nucleic acids derived from neoplastic cells). In certain
embodiments the sample is derived from a subject that is suspected
or is known to have cancer e.g. carcinoma, sarcoma, lymphoma,
leukemia, germ cell tumors, blastoma, and the like. In one
embodiment, the sample is a plasma sample derived (processed) from
peripheral blood that may comprise a mixture of cfDNA derived from
normal and cancerous cells. In another embodiment, the biological
sample that is used to determine whether a CNV is present is
derived from a cells that, if a cancer is present, comprise a
mixture of cancerous and non-cancerous cells from other biological
tissues including, but not limited to biological fluids such as
serum, sweat, tears, sputum, urine, sputum, ear flow, lymph,
saliva, cerebrospinal fluid, ravages, bone marrow suspension,
vaginal flow, transcervical lavage, brain fluid, ascites, milk,
secretions of the respiratory, intestinal and genitourinary tracts,
and leukophoresis samples, or in tissue biopsies, swabs, or smears.
In other embodiments, the biological sample is a stool (fecal)
sample.
In various embodiments the CNVs identified as indicative of the
presence of a cancer or an increased risk for a cancer include, but
are not limited to the arm level CNVs listed in Table 2. As
illustrated in Table 2 certain CNVs that comprise a substantial
arm-level gain are indicative of the presence of a cancer or an
increased risk for a certain cancers. Thus, for example, a gain in
1q is indicative of the presence or increased risk for acute
lymphoblastic leukemia (ALL), breast cancer, GIST, HCC, lung NSC,
medulloblastoma, melanoma, MPD, ovarian cancer, and/or prostate
cancer. A gain in 3q is indicative of the presence or increased
risk for Esophageal Squamous cancer, Lung SC, and/or MPD. A gain in
7q is indicative of the presence or increased risk for colorectal
cancer, glioma, HCC, lung NSC, medulloblastoma, melanoma, prostate
cancer, and/or renal cancer. A gain in 7p is indicative of the
presence or increased risk for breast cancer, colorectal cancer,
esophageal adenocarcinoma, glioma, HCC, Lung NSC, medulloblastoma,
melanoma, and/or renal cancer. A gain in 20q is indicative of the
presence or increased risk for breast cancer, colorectal cancer,
dedifferentiated liposarcoma, esophageal adenocarcinoma, esophageal
squamous, glioma cancer, HCC, lung NSC, melanoma, ovarian cancer,
and/or renal cancer, and so forth.
Similarly as illustrated in Table 2 certain CNVs that comprise a
substantial arm-level loss are indicative of the presence of and/or
an increased risk for certain cancers. Thus, for example, a loss in
1p is indicative of the presence or increased risk for
gastrointestinal stromal tumor. A loss in 4q is indicative of the
presence or increased risk for colorectal cancer, esophageal
adenocarcinoma, lung sc, melanoma, ovarian cancer, and/or renal
cancer. a loss in 17p is indicative of the presence or increased
risk for breast cancer, colorectal cancer, esophageal
adenocarcinoma, HCC, lung NSC, lung SC, and/or ovarian cancer, and
the like.
TABLE-US-00002 TABLE 2 Significant arm-level chromosomal segment
copy number alterations in each of 16 cancer subtypes (breast,
colorectal, dedifferentiated liposarcoma, esophageal
adenocarcinoma, esophageal squamous, GIST (gastrointestinal stromal
tumor), glioma, HCC (hepatocellular carcinoma), lung NSC, lung SC,
medulloblastoma, melanoma, MPD (myeloproliferative disease),
ovarian, prostate, acute lymphoblastic leukemia (ALL), and renal)
(see, e.g., Beroukhim et al. Nature (2010) 463(7283): 899-905).
Known Cancer Types Cancer Types Oncogene/Tumor Arm Significantly
Gained In Significantly Lost In Suppressor Gene 1p -- GIST 1q ALL,
Breast, GIST, HCC, Lung -- NSC, Medulloblastoma, Melanoma, MPD,
Ovarian, Prostate 3p -- Esophageal Squamous, Lung VHL NSC, Lung SC,
Renal 3q Esophageal Squamous, Lung SC, -- MPD 4p ALL Breast,
Esophageal Adenocarcinoma, Renal 4q ALL Colorectal, Esophageal
Adenocarcinoma, Lung SC, Melanoma, Ovarian, Renal 5p Esophageal
Squamous, HCC, -- TERT Lung NSC, Lung SC, Renal 5q HCC, Renal
Esophageal Adenocarcinoma, APC Lung NSC 6p ALL, HCC, Lung NSC, --
Melanoma 6q ALL Melanoma, Renal 7p Breast, Colorectal, Esophageal
-- EGFR Adenocarcinoma, Glioma, HCC, Lung NSC, Medulloblastoma,
Melanoma, Renal 7q Colorectal, Glioma, HCC, Lung -- BRAF, MET NSC,
Medulloblastoma, Melanoma, Prostate, Renal 8p ALL, MPD Breast, HCC,
Lung NSC, Medulloblastoma, Prostate, Renal 8q ALL, Breast,
Colorectal, Medulloblastoma MYC Esophageal Adenocarcinoma,
Esophageal Squamous, HCC, Lung NSC, MPD, Ovarian, Prostate 9p MPD
ALL, Breast, Esophageal CDKN2A/B Adenocarcinoma, Lung NSC,
Melanoma, Ovarian, Renal 9q ALL, MPD Lung NSC, Melanoma, Ovarian,
Renal 10p ALL Glioma, Lung SC, Melanoma 10q ALL Glioma, Lung SC,
PTEN Medulloblastoma, Melanoma 11p -- Medulloblastoma WTI 11q --
Dedifferentiated Liposarcoma, ATM Medulloblastoma, Melanoma 12p
Colorectal, Renal -- KRAS 12q Renal -- 13q Colorectal Breast,
Dedifferentiated RB1/BRCA2 Liposarcoma, Glioma, Lung NSC, Ovarian
14q ALL, Lung NSC, Lung SC, GIST, Melanoma, Renal Prostate 15q --
GIST, Lung NSC, Lung SC, Ovarian 16p Breast -- 16q -- Breast, HCC,
Medulloblastoma, Ovarian, Prostate 17p ALL Breast, Colorectal,
Esophageal TP53 Adenocarcinoma, HCC, Lung NSC, Lung SC, Ovarian 17q
ALL, HCC, Lung NSC, Breast, Ovarian ERBB2, Medulloblastoma
NF1/BRCA1 18p ALL, Medulloblastoma Colorectal, Lung NSC 18q ALL,
Medulloblastoma Colorectal, Esophageal SMAD2, SMAD4 Adenocarcinoma,
Lung NSC 19p Glioma Esophageal Adenocarcinoma, Lung NSC, Melanoma,
Ovarian 19q Glioma, Lung SC Esophageal Adenocarcinoma, Lung NSC 20p
Breast, Colorectal, Esophageal -- Adenocarcinoma, Esophageal
Squamous, GIST, Glioma, HCC, Lung NSC, Melanoma, Renal 20q Breast,
Colorectal, -- Dedifferentiated Liposarcoma, Esophageal
Adenocarcinoma, Esophageal Squamous, Glioma, HCC, Lung NSC,
Melanoma, Ovarian, Renal 21q ALL, GIST, MPD -- 22q Melanoma Breast,
Colorectal, NF2 Dedifferentiated Liposarcoma, Esophageal
Adenocarcinoma, GIST, Lung NSC, Lung SC, Ovarian, Prostate
The examples of associations between arm level copy number
variations are intended to be illustrative and not limiting. Other
arm level copy number variations and their cancer associations are
known to those of skill in the art.
Smaller, e.g., Focal, Copy Number Variations.
As indicated above, in certain embodiments, the methods described
herein can be used to determine the presence or absence of a
chromosomal amplification. In some embodiments, the chromosomal
amplification is the gain of one or more entire chromosomes. In
other embodiments, the chromosomal amplification is the gain of one
or more segments of a chromosome. In yet other embodiments, the
chromosomal amplification is the gain of two or more segments of
two or more chromosomes. In various embodiments, the chromosomal
amplification can involve the gain of one or more oncogenes.
Dominantly acting genes associated with human solid tumors
typically exert their effect by overexpression or altered
expression. Gene amplification is a common mechanism leading to
upregulation of gene expression. Evidence from cytogenetic studies
indicates that significant amplification occurs in over 50% of
human breast cancers. Most notably, the amplification of the
proto-oncogene human epidermal growth factor receptor 2 (HER2)
located on chromosome 17 (17(17q21-q22)), results in overexpression
of HER2 receptors on the cell surface leading to excessive and
dysregulated signaling in breast cancer and other malignancies
(Park et al., Clinical Breast Cancer 8:392-401 [2008]). A variety
of oncogenes have been found to be amplified in other human
malignancies. Examples of the amplification of cellular oncogenes
in human tumors include amplifications of: c-myc in promyelocytic
leukemia cell line HL60, and in small-cell lung carcinoma cell
lines, N-myc in primary neuroblastomas (stages III and IV),
neuroblastoma cell lines, retinoblastoma cell line and primary
tumors, and small-cell lung carcinoma lines and tumors, L-myc in
small-cell lung carcinoma cell lines and tumors, c-myb in acute
myeloid leukemia and in colon carcinoma cell lines, c-erbb in
epidermoid carcinoma cell, and primary gliomas, c-K-ras-2 in
primary carcinomas of lung, colon, bladder, and rectum, N-ras in
mammary carcinoma cell line (Varmus H., Ann Rev Genetics 18:
553-612 (1984) [cited in Watson et al., Molecular Biology of the
Gene (4th ed.; Benjamin/Cummings Publishing Co. 1987)].
Duplications of oncogenes are a common cause of many types of
cancer, as is the case with P70-S6 Kinase 1 amplification and
breast cancer. In such cases the genetic duplication occurs in a
somatic cell and affects only the genome of the cancer cells
themselves, not the entire organism, much less any subsequent
offspring. Other examples of oncogenes that are amplified in human
cancers include MYC, ERBB2 (EFGR), CCND1 (Cyclin D1), FGFR1 and
FGFR2 in breast cancer, MYC and ERBB2 in cervical cancer, HRAS,
KRAS, and MYB in colorectal cancer, MYC, CCND1 and MDM2 in
esophageal cancer, CCNE, KRAS and MET in gastric cancer, ERBB1, and
CDK4 in glioblastoma, CCND1, ERBB1, and MYC in head and neck
cancer, CCND1 in hepatocellular cancer, MYCB in neuroblastoma, MYC,
ERBB2 and AKT2 in ovarian cancer, MDM2 and CDK4 in sarcoma, and MYC
in small cell lung cancer. In one embodiment, the present method
can be used to determine the presence or absence of amplification
of an oncogene associated with a cancer. In some embodiments, the
amplified oncogene is associated with breast cancer, cervical
cancer, colorectal cancer, esophageal cancer, gastric cancer,
glioblastoma, head and neck cancer, hepatocellular cancer,
neuroblastoma, ovarian cancer, sarcoma, and small cell lung
cancer.
In one embodiment, the present method can be used to determine the
presence or absence of a chromosomal deletion. In some embodiments,
the chromosomal deletion is the loss of one or more entire
chromosomes. In other embodiments, the chromosomal deletion is the
loss of one or more segments of a chromosome. In yet other
embodiments, the chromosomal deletion is the loss of two or more
segments of two or more chromosomes. The chromosomal deletion can
involve the loss of one or more tumor suppressor genes.
Chromosomal deletions involving tumor suppressor genes are believed
to play an important role in the development and progression of
solid tumors. The retinoblastoma tumor suppressor gene (Rb-1),
located in chromosome 13q14, is the most extensively characterized
tumor suppressor gene. The Rb-1 gene product, a 105 kDa nuclear
phosphoprotein, apparently plays an important role in cell cycle
regulation (Howe et al., Proc Natl Acad Sci (USA) 87:5883-5887
[1990]). Altered or lost expression of the Rb protein is caused by
inactivation of both gene alleles either through a point mutation
or a chromosomal deletion. Rb-i gene alterations have been found to
be present not only in retinoblastomas but also in other
malignancies such as osteosarcomas, small cell lung cancer (Rygaard
et al., Cancer Res 50: 5312-5317 [1990)]) and breast cancer.
Restriction fragment length polymorphism (RFLP) studies have
indicated that such tumor types have frequently lost heterozygosity
at 13q suggesting that one of the Rb-1 gene alleles has been lost
due to a gross chromosomal deletion (Bowcock et al., Am J Hum
Genet, 46: 12 [1990]). Chromosome 1 abnormalities including
duplications, deletions and unbalanced translocations involving
chromosome 6 and other partner chromosomes indicate that regions of
chromosome 1, in particular 1q21-1q32 and 1p11-13, might harbor
oncogenes or tumor suppressor genes that are pathogenetically
relevant to both chronic and advanced phases of myeloproliferative
neoplasms (Caramazza et al., Eur J Hematol 84:191-200 [2010]).
Myeloproliferative neoplasms are also associated with deletions of
chromosome 5. Complete loss or interstitial deletions of chromosome
5 are the most common karyotypic abnormality in myelodysplastic
syndromes (MDSs). Isolated del(5q)/5q-MDS patients have a more
favorable prognosis than those with additional karyotypic defects,
who tend to develop myeloproliferative neoplasms (MPNs) and acute
myeloid leukemia. The frequency of unbalanced chromosome 5
deletions has led to the idea that 5q harbors one or more
tumor-suppressor genes that have fundamental roles in the growth
control of hematopoietic stem/progenitor cells (HSCs/HPCs).
Cytogenetic mapping of commonly deleted regions (CDRs) centered on
5q31 and 5q32 identified candidate tumor-suppressor genes,
including the ribosomal subunit RPS14, the transcription factor
Egr1/Krox20 and the cytoskeletal remodeling protein, alpha-catenin
(Eisenmann et al., Oncogene 28:3429-3441 [2009]). Cytogenetic and
allelotyping studies of fresh tumors and tumor cell lines have
shown that allelic loss from several distinct regions on chromosome
3p, including 3p25, 3p21-22, 3p21.3, 3p12-13 and 3p14, are the
earliest and most frequent genomic abnormalities involved in a wide
spectrum of major epithelial cancers of lung, breast, kidney, head
and neck, ovary, cervix, colon, pancreas, esophagus, bladder and
other organs. Several tumor suppressor genes have been mapped to
the chromosome 3p region, and are thought that interstitial
deletions or promoter hypermethylation precede the loss of the 3p
or the entire chromosome 3 in the development of carcinomas
(Angeloni D., Briefings Functional Genomics 6:19-39 [2007]).
Newborns and children with Down syndrome (DS) often present with
congenital transient leukemia and have an increased risk of acute
myeloid leukemia and acute lymphoblastic leukemia. Chromosome 21,
harboring about 300 genes, may be involved in numerous structural
aberrations, e.g., translocations, deletions, and amplifications,
in leukemias, lymphomas, and solid tumors. Moreover, genes located
on chromosome 21 have been identified that play an important role
in tumorigenesis. Somatic numerical as well as structural
chromosome 21 aberrations are associated with leukemias, and
specific genes including RUNX1, TMPRSS2, and TFF, which are located
in 21q, play a role in tumorigenesis (Fonatsch C Gene Chromosomes
Cancer 49:497-508 [2010]).
In view of the foregoing, in various embodiments the methods
described herein can be used to determine the segment CNVs that are
known to comprise one or more oncogenes or tumor suppressor genes,
and/or that are known to be associated with a cancer or an
increased risk of cancer. In certain embodiments, the CNVs can be
determined in a test sample comprising a constitutional (germline)
nucleic acid and the segment can be identified in those
constitutional nucleic acids. In certain embodiments segment CNVs
are identified (if present) in a sample comprising a mixture of
nucleic acids (e.g., nucleic acids derived from normal and nucleic
acids derived from neoplastic cells). In certain embodiments the
sample is derived from a subject that is suspected or is known to
have cancer e.g. carcinoma, sarcoma, lymphoma, leukemia, germ cell
tumors, blastoma, and the like. In one embodiment, the sample is a
plasma sample derived (processed) from peripheral blood that may
comprise a mixture of cfDNA derived from normal and cancerous
cells. In another embodiment, the biological sample that is used to
determine whether a CNV is present is derived from a cells that, if
a cancer is present, comprises a mixture of cancerous and
non-cancerous cells from other biological tissues including, but
not limited to biological fluids such as serum, sweat, tears,
sputum, urine, sputum, ear flow, lymph, saliva, cerebrospinal
fluid, ravages, bone marrow suspension, vaginal flow, transcervical
lavage, brain fluid, ascites, milk, secretions of the respiratory,
intestinal and genitourinary tracts, and leukophoresis samples, or
in tissue biopsies, swabs, or smears. In other embodiments, the
biological sample is a stool (fecal) sample.
The CNVs used to determine presence of a cancer and/or increased
risk for a cancer can comprise amplification or deletions.
In various embodiments the CNVs identified as indicative of the
presence of a cancer or an increased risk for a cancer include one
or more of the amplifications shown in Table 3.
TABLE-US-00003 TABLE 3 Illustrative, but non-limiting chromosomal
segments characterized by amplifications that are associated with
cancers. Cancer types listed are those identified in Beroukhim et
al. Nature 18: 463: 899-905. Cancer types identified in this
analysis Peak region Length (Mb) but not prior publications chrl:
119996566- 0.228 Breast, Lung SC, 120303234 Melanoma chrl:
148661965- 0.35 Breast, Dedifferentiated 149063439 liposarcoma,
Esophageal adenocarcinoma, Hepatocellular, Lung SC, Melanoma,
Ovarian, Prostate, Renal chr1: 1-5160566 4.416 Esophageal
adenocarcinoma, Ovarian chr1: 158317017- 1.627 Dedifferentiated
liposarcoma, 159953843 Esophageal adenocarcinoma, Prostate, Renal
chrl: 169549478- 0.889 Colorectal, Dedifferentiated 170484405
liposarcoma, Prostate, Renal chr1: 201678483- 1.471 Prostate
203358272 chr1: 241364021- 5.678 Lung NSC, Melanoma, 247249719
Ovarian chr1: 39907605- 0.319 Acute lymphoblastic 40263248
leukemia, Breast, Lung NSC, Lung SC chr1: 58658784- 1.544 Breast,
Dedifferentiated 60221344 liposarcoma, Lung SC chr3: 170024984-
3.496 Breast, Esophageal 173604597 adenocarcinoma, Glioma chr3:
178149984- 21.123 Esophageal squamous, 199501827 Lung NSC
chr3:86250885- 8.795 Lung SC, Melanoma 95164178 chr4: 54471680-
1.449 Lung NSC 55980061 chr5: 1212750-1378766 0.115
Dedifferentiated liposarcoma chr5: 174477192- 6.124 Breast, Lung
NSC 180857866 chr5: 45312870- 4.206 Lung SC 49697231 chr6:
1-23628840 23.516 Esophageal adenocarcinoma chr6: 135561194- 0.092
Breast, Esophageal 135665525 adenocarcinoma chr6: 43556800- 0.72
Esophageal 44361368 adenocarcinoma, Hepatocellular, Ovarian chr6:
63255006- 1.988 Esophageal adenocarcinoma, 65243766 Lung NSC chr7:
115981465- 0.69 Esophageal adenocarcinoma, 116676953 Lung NSC,
Melanoma, Ovarian chr7: 54899301- 0.363 Esophageal adenocarcinoma,
55275419 Esophageal squamous chr7: 89924533- 9.068 Breast,
Esophageal 98997268 adenocarcinoma, Esophageal squamous, Ovarian
chr8: 101163387- 2.516 Lung NSC, Melanoma, 103693879 Ovarian chr8:
116186189- 4.4 Breast, Hepatocellular, 120600761 Lung NSC, Ovarian
chr8: 128774432- 0.009 Esophageal adenocarcinoma, 128849112
Esophageal squamous, Hepatocellular, Lung SC, Medulloblastoma,
Myeloproliferative disorder, Ovarian chr8: 140458177- 5.784 Lung
NSC, Medulloblastoma, 146274826 Melanoma, Ovarian chr8: 38252951-
0.167 Colorectal, Esophageal 38460772 adenocarcinoma, Esophageal
squamous chr8: 42006632- 0.257 Esophageal adenocarcinoma, 42404492
Lung NSC, Lung SC, Ovarian, Prostate chr8: 81242335- 0.717 Breast,
Melanoma 81979194 chr9: 137859478- 2.29 Colorectal,
Dedifferentiated 140273252 liposarcoma chr 10: 74560456- 7.455
Breast, Ovarian, Prostate 82020637 chr11: 101433436- 0.683 Lung
NSC, Lung SC 102134907 chr11: 32027116- 5.744 Breast,
Dedifferentiated 37799354 liposarcoma, Lung NSC, Lung SC chr11:
69098089- 0.161 Dedifferentiated liposarcoma, 69278404 Esophageal
adenocarcinoma, Hepatocellular, Lung SC, Ovarian chr11: 76699529-
1.286 Dedifferentiated liposarcoma, 78005085 Esophageal
adenocarcinoma, Lung SC, Ovarian chr12: 1-1311104 1.271 Lung NSC
chr12: 25189655- 0.112 Acute lymphoblastic leukemia, 25352305
Esophageal adenocarcinoma, Esophageal squamous, Ovarian chr12:
30999223- 1.577 Acute lymphoblastic 32594050 leukemia, Colorectal,
Esophageal adenocarcinoma, Esophageal squamous, Lung NSC, Lung SC
chr12: 38788913- 3.779 Breast, Colorectal, 42596599
Dedifferentiated liposarcoma, Esophageal squamous, Lung NSC, Lung
SC chr12: 56419524- 0.021 Dedifferentiated , 56488685 liposarcoma,
Melanoma Renal chr12: 64461446- 0.041 Dedifferentiated liposarcoma,
64607139 Renal chr12: 66458200- 0.058 Dedifferentiated liposarcoma,
66543552 Esophageal squamous, Renal chr12: 67440273- 0.067 Breast,
Dedifferentiated 67566002 liposarcoma, Esophageal squamous,
Melanoma, Renal chr12: 68249634- 0.06 Breast, Dedifferentiated
68327233 liposarcoma, Esophageal squamous, Renal chr12: 70849987-
0.036 Dedifferentiated liposarcoma, 70966467 Renal chr12: 72596017-
0.23 Renal 73080626 chr12: 76852527- 0.158 Dedifferentiated
77064746 liposarcoma chr12: 85072329- 0.272 Dedifferentiated
85674601 liposarcoma chr12: 95089777- 0.161 Dedifferentiated
95350380 liposarcoma chr13: 108477140- 1.6 Breast, Esophageal
110084607 adenocarcinoma, Lung NSC, Lung SC chr13: 1-40829685
22.732 Acute lymphoblastic leukemia, Esophageal adenocarcinoma
chr13: 89500014- 3.597 Breast, Esophageal adenocarcinoma, 93206506
Medulloblastoma chr14: 106074644- 0.203 Esophageal squamous
106368585 chr14: 1-23145193 3.635 Acute lymphoblastic leukemia,
Esophageal squamous, Hepatocellular, Lung SC chr14: 35708407- 0.383
Breast, Esophageal 36097605 adenocarcinoma, Esophageal squamous,
Hepatocellular, Prostate chr15: 96891354- 0.778 Breast, Colorectal,
97698742 Esophageal adenocarcinoma, Lung NSC, Medulloblastoma,
Melanoma chr17: 18837023- 0.815 Breast, 19933105 Hepatocellular
chr17: 22479313- 0.382 Breast, 22877776 Lung NSC chr17: 24112056-
0.114 Breast, 24310787 Lung NSC chr17: 35067383- 0.149 Colorectal,
Esophageal 35272328 adenocarcinoma, Esophageal squamous chr17:
44673157- 0.351 Melanoma 45060263 chr17: 55144989- 0.31 Lung NSC,
Medulloblastoma, 55540417 Melanoma, Ovarian chr17: 62318152- 1.519
Breast, Lung NSC, Melanoma, 63890591 Ovarian chr17: 70767943- 0.537
Breast, Lung NSC, Melanoma, 71305641 Ovarian chr18: 17749667- 5.029
Colorectal, Esophageal 22797232 adenocarcinoma, Ovarian chr19:
34975531- 0.096 Breast, Esophageal 35098303 adenocarcinoma,
Esophageal squamous chr19: 43177306- 2.17 Lung NSC, 45393020
Ovarian chr19: 59066340- 0.321 Breast, Lung NSC, 59471027 Ovarian
chr2: 15977811- 0.056 Lung SC 16073001 chr20: 29526118- 0.246
Ovarian 29834552 chr20: 51603033- 0.371 Hepatocellular, Lung NSC,
51989829 Ovarian chr20: 61329497- 0.935 Hepatocellular, 62435964
Lung NSC chr22: 19172385- 0.487 Colorectal, Melanoma, 19746441
Ovarian chrX: 152729030- 1.748 Breast, Lung NSC, 154913754 Renal
chrX: 66436234- 0.267 Ovarian, Prostate 67090514
In certain embodiments in combination with the amplifications
described above (herein), or separately, the CNVs identified as
indicative of the presence of a cancer or an increased risk for a
cancer include one or more of the deletions shown in Table 4.
TABLE-US-00004 TABLE 4 Illustrative, but non-limiting chromosomal
segments characterized by deletions that are associated with
cancers. Cancer types listed are those identified in Beroukhim et
al. Nature 18: 463: 899-905. Cancer types identified in this
analysis Peak region Length (Mb) but not prior publications chr1:
110339388-119426489 1p13.2 Acute lymphoblastic leukemia, Esophageal
adenocarcinoma, Lung NSC, Lung SC, Melanoma, Ovarian, Prostate
chr1: 223876038-247249719 1q43 Acute lymphoblastic leukemia,
Breast, Lung SC, Melanoma, Prostate chr1: 26377344-27532551 1p36.11
Breast, Esophageal adenocarcinoma, Esophageal squamous, Lung NSC,
Lung SC, Medulloblastoma, Myeloproliferative disorder, Ovarian,
Prostate chr1: 3756302-6867390 1p36.31 Acute lymphoblastic
leukemia, Breast, Esophageal squamous, Hepatocellular, Lung NSC,
Lung SC, Medulloblastoma, Myeloproliferative disorder, Ovarian,
Prostate, Renal chr1: 71284749-74440273 1p31.1 Breast, Esophageal
adenocarcinoma, Glioma, Hepatocellular, Lung NSC, Lung SC,
Melanoma, Ovarian, Renal chr2: 1-15244284 2p25.3 Lung NSC, Ovarian
chr2: 138479322-143365272 2q22.1 Breast, Colorectal, Esophageal
adenocarcinoma, Esophageal squamous, Hepatocellular, Lung NSC,
Ovarian, Prostate, Renal chr2: 204533830-206266883 2q33.2
Esophageal adenocarcinoma, Hepatocellular, Lung NSC,
Medulloblastoma, Renal chr2: 241477619-242951149 2q37.3 Breast,
Dedifferentiated liposarcoma, Esophageal adenocarcinoma, Esophageal
squamous, Hepatocellular, Lung NSC, Lung SC, Medulloblastoma,
Melanoma, Ovarian, Renal chr3: 116900556-120107320 3q13.31
Dedifferentiated liposarcoma, Esophageal adenocarcinoma,
Hepatocellular, Lung NSC, Melanoma, Myeloproliferative disorder,
Prostate chr3: 1-2121282 3p26.3 Colorectal, Dedifferentiated
liposarcoma, Esophageal adenocarcinoma, Lung NSC, Melanoma,
Myeloproliferative disorder chr3: 175446835-178263192 3q26.31 Acute
lymphoblastic leukemia, Dedifferentiated liposarcoma, Esophageal
adenocarcinoma, Lung NSC, Melanoma, Myeloproliferative disorder,
Prostate chr3: 58626894-61524607 3p14.2 Breast, Colorectal,
Dedifferentiated liposarcoma, Esophageal adenocarcinoma, Esophageal
squamous, Hepatocellular, Lung NSC, Lung SC, Medulloblastoma,
Melanoma, Myeloproliferative disorder, Ovarian, Prostate, Renal
chr4: 1-435793 4p16.3 Myeloproliferative disorder chr4:
186684565-191273063 4q35.2 Breast, Esophageal adenocarcinoma,
Esophageal squamous, Lung NSC, Medulloblastoma, Melanoma, Prostate,
Renal chr4: 91089383-93486891 4q22.1 Acute lymphoblastic leukemia,
Esophageal adenocarcinoma, Hepatocellular, Lung NSC, Renal chr5:
177541057-180857866 5q35.3 Breast, Lung NSC, Myeloproliferative
disorder, Ovarian chr5: 57754754-59053198 5q11.2 Breast,
Colorectal, Dedifferentiated liposarcoma, Esophageal
adenocarcinoma, Esophageal squamous, Lung SC, Melanoma,
Myeloproliferative disorder, Ovarian, Prostate chr5:
85837489-133480433 5q21.1 Colorectal, Dedifferentiated liposarcoma,
Lung NSC, Lung SC, Myeloproliferative disorder, Ovarian chr6:
101000242-121511318 6q22.1 Colorectal, Lung NSC, Lung SC chr6:
1543157-2570302 6p25.3 Colorectal, Dedifferentiated liposarcoma,
Esophageal adenocarcinoma, Lung NSC, Lung SC, Ovarian, Prostate
chr6: 161612277-163134099 6q26 Colorectal, Esophageal
adenocarcinoma, Esophageal squamous, Lung NSC, Lung SC, Ovarian,
Prostate chr6: 76630464-105342994 6q16.1 Colorectal,
Hepatocellular, Lung NSC chr7: 141592807-142264966 7q34 Breast,
Colorectal, Esophageal adenocarcinoma, Esophageal squamous,
Hepatocellular, Lung NSC, Ovarian, Prostate, Renal chr7:
144118814-148066271 7q35 Breast, Esophageal adenocarcinoma,
Esophageal squamous, Lung NSC, Melanoma, Myeloproliferative
disorder, Ovarian chr7: 156893473-158821424 7q36.3 Breast,
Esophageal adenocarcinoma, Esophageal squamous, Lung NSC, Melanoma,
Myeloproliferative disorder, Ovarian, Prostate chr7:
3046420-4279470 7p22.2 Melanoma, Myeloproliferative disorder,
Ovarian chr7: 65877239-79629882 7q21.11 Breast, Medulloblastoma,
Melanoma, Myeloproliferative disorder, Ovarian chr8: 1-392555
8p23.3 Acute lymphoblastic leukemia, Breast, Myeloproliferative
disorder chr8: 2053441-6259545 8p23.2 Acute lymphoblastic leukemia,
Dedifferentiated liposarcoma, Esophageal adenocarcinoma, Esophageal
squamous, Hepatocellular, Lung NSC, Myeloproliferative disorder
chr8: 22125332-30139123 8p21.2 Acute lymphoblastic leukemia,
Dedifferentiated liposarcoma, Hepatocellular, Myeloproliferative
disorder, Ovarian, Renal chr8: 39008109-41238710 8p11.22 Acute
lymphoblastic leukemia, Breast, Dedifferentiated liposarcoma,
Esophageal squamous, Hepatocellular, Lung NSC, Myeloproliferative
disorder, Renal chr8: 42971602-72924037 8q11.22 Breast,
Dedifferentiated liposarcoma, Esophageal squamous, Hepatocellular,
Lung NSC, Myeloproliferative disorder, Renal chr9: 1-708871 9p24.3
Acute lymphoblastic leukemia, Breast, Lung NSC, Myeloproliferative
disorder, Ovarian, Prostate chr9: 21489625-22474701 9p21.3
Colorectal, Esophageal adenocarcinoma, Esophageal squamous,
Myeloproliferative disorder, Ovarian chr9: 36365710-37139941 9p13.2
Myeloproliferative disorder chr9: 7161607-12713130 9p24.1 Acute
lymphoblastic leukemia, Breast, Colorectal, Esophageal
adenocarcinoma, Hepatocellular, Lung SC, Medulloblastoma, Melanoma,
Myeloproliferative disorder, Ovarian, Prostate, Renal chr10:
1-1042949 10p15.3 Colorectal, Lung NSC, Lung SC, Ovarian, Prostate,
Renal chr10: 129812260-135374737 10q26.3 Breast, Colorectal,
Glioma, Lung NSC, Lung SC, Melanoma, Ovarian, Renal chr10:
52313829-53768264 10q11.23 Colorectal, Lung NSC, Lung SC, Ovarian,
Renal chr10: 89467202-90419015 10q23.31 Breast, Lung SC, Ovarian,
Renal chr11: 107086196-116175885 11q23.1 Esophageal adenocarcinoma,
Medulloblastoma, Renal chr11: 1-1391954 11p15.5 Breast,
Dedifferentiated liposarcoma, Esophageal adenocarcinoma, Lung NSC,
Medulloblastoma, Ovarian chr11: 130280899-134452384 11q25
Esophageal adenocarcinoma, Esophageal squamous, Hepatocellular,
Lung NSC, Medulloblastoma, Renal chr11: 82612034-85091467 11q14.1
Melanoma, Renal chr12: 11410696-12118386 12p13.2 Breast,
Hepatocellular, Myeloproliferative disorder, Prostate chr12:
131913408-132349534 12q24.33 Dedifferentiated liposarcoma, Lung
NSC, Myeloproliferative disorder chr12: 97551177-99047626 12q23.1
Breast, Colorectal, Esophageal squamous, Lung NSC,
Myeloproliferative disorder chr13: 111767404-114142980 13q34
Breast, Hepatocellular, Lung NSC chr13: 1-23902184 13q12.11 Breast,
Lung SC, Ovarian chr13: 46362859-48209064 13q14.2 Hepatocellular,
Lung SC, Myeloproliferative disorder, Prostate chr13:
92308911-94031607 13q31.3 Breast, Hepatocellular, Lung NSC, Renal
chr14: 1-29140968 14q11.2 Acute lymphoblastic leukemia, Esophageal
adenocarcinoma, Myeloproliferative disorder chr14:
65275722-67085224 14q23.3 Dedifferentiated liposarcoma,
Myeloproliferative disorder chr14: 80741860-106368585 14q32.12
Acute lymphoblastic leukemia, Dedifferentiated liposarcoma,
Melanoma, Myeloproliferative disorder chr15: 1-24740084 15q11.2
Acute lymphoblastic leukemia, Breast, Esophageal adenocarcinoma,
Lung NSC, Myeloproliferative disorder, Ovarian chr15:
35140533-43473382 15q15.1 Esophageal adenocarcinoma, Lung NSC,
Myeloproliferative disorder chr16: 1-359092 16p13.3 Esophageal
adenocarcinoma, Hepatocellular, Lung NSC, Renal chr16:
31854743-53525739 16q11.2 Breast, Hepatocellular, Lung NSC,
Melanoma, Renal chr16: 5062786-7709383 16p13.3 Hepatocellular, Lung
NSC, Medulloblastoma, Melanoma, Myeloproliferative disorder,
Ovarian, Renal chr16: 76685816-78205652 16q23.1 Breast, Colorectal,
Esophageal adenocarcinoma, Hepatocellular, Lung NSC, Lung SC,
Medulloblastoma, Renal chr16: 80759878-82408573 16q23.3 Colorectal,
Hepatocellular, Renal chr16: 88436931-88827254 16q24.3 Colorectal,
Hepatocellular, Lung NSC, Prostate, Renal chr17: 10675416-12635879
17p12 Lung NSC, Lung SC, Myeloproliferative disorder chr17:
26185485-27216066 17q11.2 Breast, Colorectal, Dedifferentiated
liposarcoma, Lung NSC, Lung SC, Melanoma, Myeloproliferative
disorder, Ovarian chr17: 37319013-37988602 17q21.2 Breast,
Colorectal, Dedifferentiated liposarcoma, Lung SC, Melanoma,
Myeloproliferative disorder, Ovarian chr17: 7471230-7717938 17p13.1
Lung SC, Myeloproliferative disorder chr17: 78087533-78774742
17q25.3 Colorectal, Myeloproliferative disorder chr18: 1-587750
18p11.32 Myeloproliferative disorder chr18: 46172638-49935241
18q21.2 Esophageal adenocarcinoma, Lung NSC chr18:
75796373-76117153 18q23 Colorectal, Esophageal adenocarcinoma,
Esophageal squamous, Ovarian, Prostate chr19: 1-526082 19p13.3
Hepatocellular, Lung NSC, Renal chr19: 21788507-34401877 19p12
Hepatocellular, Lung NSC, Renal chr19: 52031294-53331283 19q13.32
Breast, Hepatocellular, Lung NSC, Medulloblastoma, Ovarian, Renal
chr19: 63402921-63811651 19q13.43 Breast, Colorectal,
Dedifferentiated liposarcoma, Hepatocellular, Lung NSC,
Medulloblastoma, Ovarian, Renal chr20: 1-325978 20p13 Breast,
Dedifferentiated liposarcoma, Lung NSC chr20: 14210829-15988895
20p12.1 Esophageal adenocarcinoma, Lung NSC, Medulloblastoma,
Melanoma, Myeloproliferative disorder, Prostate, Renal chr21:
38584860-42033506 21q22.2 Breast chr22: 20517661-21169423 22q11.22
Acute lymphoblastic leukemia, Esophageal adenocarcinoma chr22:
45488286-49691432 22q13.33 Breast, Hepatocellular, Lung NSC, Lung
SC chrX: 1-3243111 Xp22.33 Esophageal adenocarcinoma, Lung NSC,
Lung SC chrX: 31041721-34564697 Xp21.2 Acute lymphoblastic
leukemia, Esophageal adenocarcinoma, Glioma
The anuploidies identified as characteristic of various cancers
(e.g., the anuploidies identified in Tables 3 and 4) may contain
genes known to be implicated in cancer etiologies (e.g., tumor
suppressors, oncogenes, etc.). These aneuploidies can also be
probed to identify relevant but previously unknown genes.
For example Beroukhim et al. supra, assessed potential
cancer-causing genes in the copy number alterations using GRAIL
(Gene Relationships Among Implicated Loci.sub.20), an algorithm
that searches for functional relationships among genomic regions.
GRAIL scores each gene in a collection of genomic regions for its
`relatedness` to genes in other regions based on textual similarity
between published abstracts for all papers citing the genes, on the
notion that some target genes will function in common pathways.
These methods permit identification/characterization of genes
previously not associated with the particular cancers at issue.
Table 5 illustrates target genes known to be within the identified
amplified segment and predicted genes, and Table 6 illustrates
target genes known to be within the identified deleted segment and
predicted genes.
TABLE-US-00005 TABLE 5 Illustrative, but non-limiting chromosomal
segments and genes known or predicted to be present in regions
characterized by amplification in various cancers (see, e.g.,
Beroukhim et al. supra.). Chromosome GRAIL top and band Peak region
# genes Known target target 8q24.21 chr8: 128774432-128849112 1 MYC
MYC 11q13.2 chr11: 69098089-69278404 3 CCND1 ORAOV1 17q12 chr17:
35067383-35272328 6 ERBB2 ERBB2, C17orf37 12q14.1 chr12:
56419524-56488685 7 CDK4 TSPAN31 14q13.3 chr14: 35708407-36097605 3
NKX2-1 NKX2-1 12q15 chr12: 67440273-67566002 1 MDM2 MDM2 7p11.2
chr7: 54899301-55275419 1 EGFR EGFR 1q21.2 chr1:
148661965-149063439 9 MCL1.dagger-dbl. MCL1 8p12 chr8:
38252951-38460772 3 FGFR1 FGFR1 12p12.1 chr12: 25189655-25352305 2
KRAS KRAS 19q12 chr19: 34975531-35098303 1 CCNE1 CCNE1 22q11.21
chr22: 19172385-19746441 11 CRKL CRKL 12q15 chr12:
68249634-68327233 2 LRRC10 12q14.3 chr12: 64461446-64607139 1 HMGA2
HMGA2 Xq28 chrX: 152729030-154913754 53 SPRY3 5p15.33 chr5:
1212750-1378766 3 TERT TERT 3q26.2 chr3: 170024984-173604597 22
PRKCI PRKCI 15q26.3 chr15: 96891354-97698742 4 IGF1R IGF1R 20q13.2
chr20: 51603033-51989829 1 ZNF217 8p11.21 chr8: 42006632-42404492 6
PLAT 1p34.2 chr1: 39907605-40263248 7 MYCL1 MYCL1 17q21.33 chr17:
44673157-45060263 4 NGFR, PHB 2p24.3 chr2: 15977811-16073001 1 MYCN
MYCN 7q21.3 chr7: 89924533-98997268 62 CDK6 CDK6 13q34 chr13:
108477140-110084607 4 IRS2 11q14.1 chr11: 76699529-78005085 14 GAB2
20q13.33 chr20: 61329497-62435964 38 BIRC7 17q23.1 chr17:
55144989-55540417 5 RPS6KB1 1p12 chr1: 119996566-120303234 5 REG4
8q21.13 chr8: 81242335-81979194 3 ZNF704, ZBTB10 6p21.1 chr6:
43556800-44361368 18 VEGFA 5p11 chr5: 45312870-49697231 0 20q11.21
chr20: 29526118-29834552 5 BCL2L1.dagger-dbl. BCL2L1, ID1 6q23.3
chr6: 135561194-135665525 1 MYB** hsa-mir-548a-2 1q44 chr1:
241364021-247249719 71 AKT3 5q35.3 chr5: 174477192-180857866 92
FLT4 7q31.2 chr7: 115981465-116676953 3 MET MET 18q11.2 chr18:
17749667-22797232 21 CABLES1 17q25.1 chr17: 70767943-71305641 13
GRB2, ITGB4 1p32.1 chr1: 58658784-60221344 7 JUN JUN 17q11.2 chr17:
24112056-24310787 5 DHRS13, FLOT2, ERAL1, PHF12 17p11.2 chr17:
18837023-19933105 12 MAPK7 8q24.11 chr8: 116186189-120600761 13 NOV
12q15 chr12: 66458200-66543552 0 19q13.2 chr19: 43177306-45393020
60 LGALS7, DYRK1B 11q22.2 chr11: 101433436-102134907 8 BIRC2, BIRC2
YAP1 4q12 chr4: 54471680-55980061 7 PDGFRA, KDR, KIT KIT 12p11.21
chr12: 30999223-32594050 9 DDX11, FAM60A 3q28 chr3:
178149984-199501827 143 PIK3CA PIK3CA 1p36.33 chr1: 1-5160566 77
TP73 17q24.2 chr17: 62318152-63890591 12 BPTF 1q23.3 chr1:
158317017-159953843 52 PEA15 1q24.3 chr1: 169549478-170484405 6
BAT2D1, MYOC 8q22.3 chr8: 101163387-103693879 14 RRM2B 13q31.3
chr13: 89500014-93206506 3 GPC5 12q21.1 chr12: 70849987-70966467 0
12p13.33 chr12: 1-1311104 10 WNK1 12q21.2 chr12: 76852527-77064746
0 1q32.1 chr1: 201678483-203358272 21 MDM4 MDM4 19q13.42 chr19:
59066340-59471027 19 PRKCG, TSEN34 12q12 chr12: 38788913-42596599
12 ADAMTS20 12q23.1 chr12: 95089777-95350380 2 ELK3 12q21.32 chr12:
85072329-85674601 0 10q22.3 chr10: 74560456-82020637 46 SFTPA1B
3p11.1 chr3: 86250885-95164178 8 POU1F1 17q11.1 chr17:
22479313-22877776 1 WSB1 8q24.3 chr8: 140458177-146274826 97
PTP4A3, MAFA, PARP10 Xq12 chrX: 66436234-67090514 1 AR AR 6q12
chr6: 63255006-65243766 3 PTP4A1 14q11.2 chr14: 1-23145193 95
BCL2L2 9q34.3 chr9: 137859478-140273252 76 NRARP, MRPL41, TRAF2,
LHX3 6p24.1 chr6: 1-23628840 95 E2F3 13q12.2 chr13: 1-40829685 110
FOXO1 12q21.1 chr12: 72596017-73080626 0 14q32.33 chr14:
106074644-106368585 0 11p13 chr11: 32027116-37799354 35 WT1
TABLE-US-00006 TABLE 6 Illustrative, but non-limiting chromosomal
segments and genes known or predicted to be present in regions
charactierzed by amplification in various cancers (see, e.g.,
Beroukhim et al. supra.). Chromosome # and band Peak region genes
Known target GRAIL top target 9p21.3 chr9: 21489625-22474701 5
CDKN2A/B CDKN2A 3p14.2 chr3: 58626894-61524607 2 FHIT.sctn. FHIT
16q23.1 chr16: 76685816-78205652 2 WWOX.sctn. WWOX 9p24.1 chr9:
7161607-12713130 3 PTPRD.sctn. PTPRD 20p12.1 chr20:
14210829-15988895 2 MACROD2.sctn. FLRT3 6q26 chr6:
161612277-163134099 1 PARK2.sctn. PARK2 13q14.2 chr13:
46362859-48209064 8 RB1 RB1 2q22.1 chr2: 138479322-143365272 3
LRP1B.sctn. LRP1B 4q35.2 chr4: 186684565-191273063 15 FRG2, TUBB4Q
5q11.2 chr5: 57754754-59053198 5 PDE4D.sctn. PLK2, PDE4D 16p13.3
chr16: 5062786-7709383 2 A2BP1.sctn. A2BP1 7q34 chr7:
141592807-142264966 3 TRB@{circumflex over ( )} PRSS1 2q37.3 chr2:
241477619-242951149 19 TMEM16G, ING5 19p13.3 chr19: 1-526082 10
GZMM, THEG, PPAP2C, C19orf20 10q23.31 chr10: 89467202-90419015 4
PTEN PTEN 8p23.2 chr8: 2053441-6259545 1 CSMD1.sctn. CSMD1 1p36.31
chr1: 3756302-6867390 23 DFFB, ZBTB48, AJAP1 4q22.1 chr4:
91089383-93486891 2 MGC48628 18q23 chr18: 75796373-76117153 4
PARD6G 6p25.3 chr6: 1543157-2570302 2 FOXC1 19q13.43 chr19:
63402921-63811651 17 ZNF324 Xp21.2 chrX: 31041721-34564697 2
DMD.sctn. DMD 11q25 chr11: 130280899-134452384 12 OPCML.sctn., HNT
HNT.sctn. 13q12.11 chr13: 1-23902184 29 LATS2 22q13.33 chr22:
45488286-49691432 38 TUBGCP6 15q11.2 chr15: 1-24740084 20 A26B1
22q11.22 chr22: 20517661-21169423 3 VPREB1 10q26.3 chr10:
129812260-135374737 35 MGMT, SYCE1 12p13.2 chr12: 11410696-12118386
2 ETV6$ ETV6 8p23.3 chr8: 1-392555 2 ZNF596 1p36.11 chr1:
26377344-27532551 24 SFN 11p15.5 chr11: 1-1391954 49 RASSF7 17q11.2
chr17: 26185485-27216066 10 NF1 NF1 11q23.1 chr11:
107086196-116175885 61 ATM CADM1 9p24.3 chr9: 1-708871 5 FOXD4
10q11.23 chr10: 52313829-53768264 4 PRKG1.sctn. DKK1, PRKG1 15q15.1
chr15: 35140533-43473382 109 TUBGCP4 1p13.2 chr1:
110339388-119426489 81 MAGI3 Xp22.33 chrX: 1-3243111 21 SHOX 3p26.3
chr3: 1-2121282 2 CHL1 9p13.2 chr9: 36365710-37139941 2 PAX5 MELK
17p13.1 chr17: 7471230-7717938 10 TP53 ATP1B2 12q24.33 chr12:
131913408-132349534 7 CHFR 7q36.3 chr7: 156893473-158821424 7
PTPRN2.sctn. NCAPG2 6q16.1 chr6: 76630464-105342994 76 FUT9,
C6orf165, C6orf162, GJA10 5q21.1 chr5: 85837489-133480433 142 APC
APC 8p11.22 chr8: 39008109-41238710 7 C8orf4, ZMAT4 19q13.32 chr19:
52031294-53331283 25 BBC3 10p15.3 chr10: 1-1042949 4 TUBB8 1p31.1
chr1: 71284749-74440273 4 NEGR1.sctn. NEGR1 13q31.3 chr13:
92308911-94031607 2 GPC6.sctn. GPC6, DCT 16q11.2 chr16:
31854743-53525739 37 RBL2 20p13 chr20: 1-325978 10 SOX12 5q35.3
chr5: 177541057-180857866 43 SCGB3A1 1q43 chr1: 223876038-247249719
173 RYR2.sctn. FH, ZNF678 16p13.3 chr16: 1-359092 16 HBZ 17q21.2
chr17: 37319013-37988602 22 CNP 2p25.3 chr2: 1-15244284 51 MYT1L
3q13.31 chr3: 116900556-120107320 1 LSAMP 7q21.11 chr7:
65877239-79629882 73 MAGI2.sctn. CLDN4 7q35 chr7:
144118814-148066271 3 CNTNAP2.sctn. CNTNAP2 14q32.12 chr14:
80741860-106368585 154 PRIMA1 16q24.3 chr16: 88436931-88827254 9
C16orf3 3q26.31 chr3: 175446835-178263192 1 NAALADL2.sctn. NAALADL2
17q25.3 chr17: 78087533-78774742 8 ZNF750 19p12 chr19:
21788507-34401877 12 ZNF492, ZNF99 12q23.1 chr12: 97551177-99047626
3 ANKS1B.sctn. ANKS1B 4p16.3 chr4: 1-435793 4 ZNF141 18p11.32
chr18: 1-587750 4 COLEC12 2q33.2 chr2: 204533830-206266883 1
PARD3B.sctn. PARD3B 8p21.2 chr8: 22125332-30139123 63 DPYSL2, STMN4
8q11.22 chr8: 42971602-72924037 86 SNTG1.sctn. FLJ23356, ST18,
RB1CC1 16q23.3 chr16: 80759878-82408573 2 CDH13.sctn. CDH13 11q14.1
chr11: 82612034-85091467 6 DLG2.sctn. CCDC89, CCDC90B, TMEM126A
14q23.3 chr14: 65275722-67085224 7 GPHN, MPP5 7p22.2 chr7:
3046420-4279470 1 SDK1.sctn. SDK1 13q34 chr13: 111767404-114142980
25 TUBGCP3 17p12 chr17: 10675416-12635879 5 MAP2K4 MAP2K4, ZNF18
21q22.2 chr21: 38584860-42033506 19 DSCAM.sctn., DSCAM TMPRSS2/
ERG$ 18q21.2 chr18: 46172638-49935241 7 SMAD4, DCC DCC.sctn. 6q22.1
chr6: 101000242-121511318 87 GTF3C6, TUBE1, ROS1 14q11.2 chr14:
1-29140968 140 ZNF219, NDRG2
In various embodiments, it is contemplated to use the methods
identified herein to identify CNVs of segments comprising the
amplified regions or genes identified in Table 5 and/or to use the
methods identified herein to identify CNVs of segments comprising
the deleted regions or genes identified in 6.
In one embodiment, the methods described herein provide a means to
assess the association between gene amplification and the extent of
tumor evolution. Correlation between amplification and/or deletion
and stage or grade of a cancer may be prognostically important
because such information may contribute to the definition of a
genetically based tumor grade that would better predict the future
course of disease with more advanced tumors having the worst
prognosis. In addition, information about early amplification
and/or deletion events may be useful in associating those events as
predictors of subsequent disease progression.
Gene amplification and deletions as identified by the method can be
associated with other known parameters such as tumor grade,
histology, Brd/Urd labeling index, hormonal status, nodal
involvement, tumor size, survival duration and other tumor
properties available from epidemiological and biostatistical
studies. For example, tumor DNA to be tested by the method could
include atypical hyperplasia, ductal carcinoma in situ, stage I-III
cancer and metastatic lymph nodes in order to permit the
identification of associations between amplifications and deletions
and stage. The associations made may make possible effective
therapeutic intervention. For example, consistently amplified
regions may contain an overexpressed gene, the product of which may
be able to be attacked therapeutically (for example, the growth
factor receptor tyrosine kinase, p185.sup.HER2).
In various embodiments, the methods described herein can be used to
identify amplification and/or deletion events that are associated
with drug resistance by determining the copy number variation of
nucleic acid sequences from primary cancers to those of cells that
have metastasized to other sites. If gene amplification and/or
deletion is a manifestation of karyotypic instability that allows
rapid development of drug resistance, more amplification and/or
deletion in primary tumors from chemoresistant patients than in
tumors in chemosensitive patients would be expected. For example,
if amplification of specific genes is responsible for the
development of drug resistance, regions surrounding those genes
would be expected to be amplified consistently in tumor cells from
pleural effusions of chemoresistant patients but not in the primary
tumors. Discovery of associations between gene amplification and/or
deletion and the development of drug resistance may allow the
identification of patients that will or will not benefit from
adjuvant therapy.
In a manner similar to that described for determining the presence
or absence of complete and/or partial fetal chromosomal
aneuploidies in a maternal sample, methods, apparatus, and systems
described herein can be used to determine the presence or absence
of complete and/or partial chromosomal aneuploidies in any patient
sample comprising nucleic acids e.g. DNA or cfDNA (including
patient samples that are not maternal samples). The patient sample
can be any biological sample type as described elsewhere herein.
Preferably, the sample is obtained by non-invasive procedures. For
example, the sample can be a blood sample, or the serum and plasma
fractions thereof. Alternatively, the sample can be a urine sample
or a fecal sample. In yet other embodiments, the sample is a tissue
biopsy sample. In all cases, the sample comprises nucleic acids
e.g. cfDNA or genomic DNA, which is purified, and sequenced using
any of the NGS sequencing methods described previously.
Both complete and partial chromosomal aneuploidies associated with
the formation, and progression of cancer can be determined
according to the present method.
In various embodiments, when using the methods described herein to
determine the presence and/or increased risk of cancer
normalization of the data can be made with respect to the
chromosome(s) for which the CNV is determined. In certain
embodiments normalization of the data can be made with respect to
the chromosome arm(s) for which the CNV is determined. In certain
embodiments, normalization of the data can be made with respect to
the particular segment(s) for which the CNV is determined.
In addition to the role of CNV in cancer, CNVs have been associated
with a growing number of common complex disease, including human
immunodeficiency virus (HIV), autoimmune diseases and a spectrum of
neuropsychiatric disorders.
CNVs in Infectious and Autoimmune Disease
To date a number of studies have reported association between CNV
in genes involved in inflammation and the immune response and HIV,
asthma, Crohn's disease and other autoimmune disorders (Fanciulli
et al., Clin Genet 77:201-213 [2010]). For example, CNV in CCL3L1,
has been implicated in HIV/AIDS susceptibility (CCL3L1, 17q11.2
deletion), rheumatoid arthritis (CCL3L1, 17q11.2 deletion), and
Kawasaki disease (CCL3L1, 17q11.2 duplication); CNV in HBD-2, has
been reported to predispose to colonic Crohn's disease (HDB-2,
8p23.1 deletion) and psoriasis (HDB-2, 8p23.1 deletion); CNV in
FCGR3B, was shown to predispose to glomerulonephritis in systemic
lupus erthematosous (FCGR3B, 1q23 deletion, 1q23 duplication),
anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculatis
(FCGR3B, 1q23 deletion), and increase the risk of developing
rheumatoid arthritis. There are at least two inflammatory or
autoimmune diseases that have been shown to be associated with CNV
at different gene loci. For example, Crohn's disease is associated
with low copy number at HDB-2, but also with a common deletion
polymorphism upstream of the IGRM gene that encodes a member of the
p47 immunity-related GTPase family. In addition to the association
with FCGR3B copy number, SLE susceptibility has also been reported
to be significantly increased among subjects with a lower number of
copies of complement component C4.
Associations between genomic deletions at the GSTM1 (GSTM1,
1q23deletion) and GSTT1 (GSTT1, 22q11.2 deletion) loci and
increased risk of atopic asthma have been reported in a number of
independent studies. In some embodiments, the methods described
herein can be used to determine the presence or absence of a CNV
associated with inflammation and/or autoimmune diseases. For
example, the methods can be used to determine the presence of a CNV
in a patient suspected to be suffering from HIV, asthma, or Crohn's
disease. Examples of CNV associated with such diseases include
without limitation deletions at 17q11.2, 8p23.1, 1q23, and 22q11.2,
and duplications at 17q11.2, and 1q23. In some embodiments, the
present method can be used to determine the presence of CNV in
genes including but not limited to CCL3L1, HBD-2, FCGR3B, GSTM,
GSTT1, C4, and IRGM.
CNV Diseases of the Nervous System
Associations between de novo and inherited CNV and several common
neurological and psychiatric diseases have been reported in autism,
schizophrenia and epilepsy, and some cases of neurodegenerative
diseases such as Parkinson's disease, amyotrophic lateral sclerosis
(ALS) and autosomal dominant Alzheimer's disease (Fanciulli et al.,
Clin Genet 77:201-213 [2010]). Cytogenetic abnormalities have been
observed in patients with autism and autism spectrum disorders
(ASDs) with duplications at 15q11-q13. According to the Autism
Genome project Consortium, 154 CNV including several recurrent
CNVs, either on chromosome 15q11-q13 or at new genomic locations
including chromosome 2p16, 1q21 and at 17p12 in a region associated
with Smith-Magenis syndrome that overlaps with ASD. Recurrent
microdeletions or microduplications on chromosome 16p11.2 have
highlighted the observation that de novo CNVs are detected at loci
for genes such as SHANK3 (22q13.3 deletion), neurexin 1 (NRXN1,
2p16.3 deletion) and the neuroglins (NLGN4, Xp22.33 deletion) that
are known to regulate synaptic differentiation and regulate
glutaminergic neurotransmitter release. Schizophrenia has also been
associated with multiple de novo CNVs. Microdeletions and
microduplications associated with schizophrenia contain an
overrepresentation of genes belonging to neurodevelopmental and
glutaminergic pathways, suggesting that multiple CNVs affecting
these genes may contribute directly to the pathogenesis of
schizophrenia e.g. ERBB4, 2q34 deletion, SLC1A3, 5p13.3 deletion;
RAPEGF4, 2q31.1 deletion; CIT, 12.24 deletion; and multiple genes
with de novo CNV. CNVs have also been associated with other
neurological disorders including epilepsy (CHRNA7, 15q13.3
deletion), Parkinson's disease (SNCA 4q22 duplication) and ALS
(SMN1, 5q12.2.-q13.3 deletion; and SMN2 deletion). In some
embodiments, the methods described herein can be used to determine
the presence or absence of a CNV associated with diseases of the
nervous system. For example, the methods can be used to determine
the presence of a CNV in a patient suspected to be suffering from
autisim, schizophrenia, epilepsy, neurodegenerative diseases such
as Parkinson's disease, amyotrophic lateral sclerosis (ALS) or
autosomal dominant Alzheimer's disease. The methods can be used to
determine CNV of genes associated with diseases of the nervous
system including without limitation any of the Autism Spectrum
Disorders (ASD), schizophrenia, and epilepsy, and CNV of genes
associated with neurodegenerative disorders such as Parkinson's
disease. Examples of CNV associated with such diseases include
without limitation duplications at 15q11-q13, 2p16, 1q21, 17p12,
16p11.2, and 4q22, and deletions at 22q13.3, 2p16.3, Xp22.33, 2q34,
5p13.3, 2q31.1, 12.24, 15q13.3, and 5q12.2. In some embodiments,
the methods can be used to determine the presence of CNV in genes
including but not limited to SHANK3, NLGN4, NRXN1, ERBB4, SLC1A3,
RAPGEF4, CIT, CHRNA7, SNCA, SMN1, and SMN2.
CNV and Metabolic or Cardiovascular Diseases
The association between metabolic and cardiovascular traits, such
as familial hypercholesterolemia (FH), atherosclerosis and coronary
artery disease, and CNVs has been reported in a number of studies
(Fanciulli et al., Clin Genet 77:201-213 [2010]). For example,
germline rearrangements, mainly deletions, have been observed at
the LDLR gene (LDLR, 19p13.2 deletion/duplication) in some FH
patients who carry no other LDLR mutations. Another example is the
LPA gene that encodes apolipoprotein(a) (apo(a)) whose plasma
concentration is associated with risk of coronary artery disease,
myocardial infarction (MI) and stroke. Plasma concentrations of the
apo(a) containing lipoprotein Lp(a) vary over 1000-fold between
individuals and 90% of this variability is genetically determined
at the LPA locus, with plasma concentration and Lp(a) isoform size
being proportional to a highly variable number of `kringle 4`
repeat sequences (range 5-50). These data indicate that CNV in at
least two genes can be associated with cardiovascular risk. The
methods described herein can be used in large studies to search
specifically for CNV associations with cardiovascular disorders. In
some embodiments, the present method can be used to determine the
presence or absence of a CNV associated with metabolic or
cardiovascular disease. For example, the present method can be used
to determine the presence of a CNV in a patient suspected to be
suffering from familial hypercholesterolemia. The methods described
herein can be used to determine CNV of genes associated with
metabolic or cardiovascular disease e.g. hypercholesterolemia.
Examples of CNV associated with such diseases include without
limitation 19p13.2 deletion/duplication of the LDLR gene, and
multiplications in the LPA gene.
Determination of Complete Chromosomal Aneuploidies in Patient
Samples
In one embodiment, method are provided for determining the presence
or absence of any one or more different complete chromosomal
aneuploidies in a patient test sample comprising nucleic acid
molecules. In some embodiments, the method determines the presence
or absence of any one or more different complete chromosomal
aneuploidies. The steps of the method comprise (a) obtaining
sequence information for the patient nucleic acids in the patient
test sample; and (b) using the sequence information to identify a
number of sequence tags for each of any one or more chromosomes of
interest selected from chromosomes 1-22, X and Y and to identify a
number of sequence tags for a normalizing chromosome sequence for
each of the any one or more chromosomes of interest. The
normalizing chromosome sequence can be a single chromosome, or it
can be a group of chromosomes selected from chromosomes 1-22, X,
and Y. The method further uses in step (c) the number of sequence
tags identified for each of the any one or more chromosomes of
interest and the number of sequence tags identified for each
normalizing chromosome sequence to calculate a single chromosome
dose for each of the any one or more chromosomes of interest; and
(d) compares each of the single chromosome doses for each of the
any one or more chromosomes of interest to a threshold value for
each of the one or more chromosomes of interest, thereby
determining the presence or absence of any one or more different
complete patient chromosomal aneuploidies in the patient test
sample.
In some embodiments, step (c) comprises calculating a single
chromosome dose for each chromosomes of interest as the ratio of
the number of sequence tags identified for each of the chromosomes
of interest and the number of sequence tags identified for the
normalizing chromosome for each of the chromosomes of interest.
In other embodiments, step (c) comprises calculating a single
chromosome dose for each of the chromosomes of interest as the
ratio of the number of sequence tags identified for each of the
chromosomes of interest and the number of sequence tags identified
for the normalizing chromosome for each of the chromosomes of
interest. In other embodiments, step (c) comprises calculating a
sequence tag ratio for a chromosome of interest by relating the
number of sequence tags obtained for the chromosome of interest to
the length of the chromosome of interest, and relating the number
of tags for the corresponding normalizing chromosome sequence for
the chromosome of interest to the length of the normalizing
chromosome sequence, and calculating a chromosome dose for the
chromosome of interest as a ratio of the sequence tags density of
the chromosome of interest and the sequence tag density for the
normalizing sequence. The calculation is repeated for each of all
chromosomes of interest. Steps (a)-(d) can be repeated for test
samples from different patients.
An example of the embodiment whereby one or more complete
chromosomal aneuploidies are determined in a cancer patient test
sample comprising cell-free DNA molecules, comprises: (a)
sequencing at least a portion of cell-free DNA molecules to obtain
sequence information for the patient cell-free DNA molecules in the
test sample; (b) using the sequence information to identify a
number of sequence tags for each of any twenty or more chromosomes
of interest selected from chromosomes 1-22, X, and Y and to
identify a number of sequence tags for a normalizing chromosome for
each of the twenty or more chromosomes of interest; (c) using the
number of sequence tags identified for each of the twenty or more
chromosomes of interest and the number of sequence tags identified
for each the normalizing chromosome to calculate a single
chromosome dose for each of the twenty or more chromosomes of
interest; and (d) comparing each of the single chromosome doses for
each of the twenty or more chromosomes of interest to a threshold
value for each of the twenty or more chromosomes of interest, and
thereby determining the presence or absence of any twenty or more
different complete chromosomal aneuploidies in the patient test
sample.
In another embodiment, the method for determining the presence or
absence of any one or more different complete chromosomal
aneuploidies in a patient test sample as described above uses a
normalizing segment sequence for determining the dose of the
chromosome of interest. In this instance, the method comprises (a)
obtaining sequence information for the nucleic acids in the sample;
(b) using the sequence information to identify a number of sequence
tags for each of any one or more chromosomes of interest selected
from chromosomes 1-22, X and Y and to identify a number of sequence
tags for a normalizing segment sequence for each of any one or more
chromosomes of interest. The normalizing segment sequence can be a
single segment of a chromosome or it can be a group of segments
form one or more different chromosomes. The method further uses in
step (c) the number of sequence tags identified for each of said
any one or more chromosomes of interest and said number of sequence
tags identified for said normalizing segment sequence to calculate
a single chromosome dose for each of said any one or more
chromosomes of interest; and (d) comparing each of said single
chromosome doses for each of said any one or more chromosomes of
interest to a threshold value for each of said one or more
chromosomes of interest, and thereby determining the presence or
absence of one or more different complete chromosomal aneuploidies
in the patient sample.
In some embodiments, step (c) comprises calculating a single
chromosome dose for each of said chromosomes of interest as the
ratio of the number of sequence tags identified for each of said
chromosomes of interest and the number of sequence tags identified
for said normalizing segment sequence for each of said chromosomes
of interest.
In other embodiments, step (c) comprises calculating a sequence tag
ratio for a chromosome of interest by relating the number of
sequence tags obtained for the chromosome of interest to the length
of the chromosome of interest, and relating the number of tags for
the corresponding normalizing segment sequence for the chromosome
of interest to the length of the normalizing segment sequence, and
calculating a chromosome dose for the chromosome of interest as a
ratio of the sequence tags density of the chromosome of interest
and the sequence tag density for the normalizing segment sequence.
The calculation is repeated for each of all chromosomes of
interest. Steps (a)-(d) can be repeated for test samples from
different patients.
A means for comparing chromosome doses of different sample sets is
provided by determining a normalized chromosome value (NCV), which
relates the chromosome dose in a test sample to the mean of the of
the corresponding chromosome dose in a set of qualified samples.
The NCV is calculated as:
.mu..sigma. ##EQU00008## where {circumflex over (.mu.)}.sub.j and
{circumflex over (.sigma.)}.sub.j are the estimated mean and
standard deviation, respectively, for the j-th chromosome dose in a
set of qualified samples, and x.sub.ij is the observed j-th
chromosome dose for test sample i.
In some embodiments, the presence or absence of one complete
chromosomal aneuploidy is determined. In other embodiments, the
presence or absence of two, three, four, five, six, seven, eight,
nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen,
seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two,
twenty-three, or twenty four complete chromosomal aneuploidies are
determined in a sample, wherein twenty-two of the complete
chromosomal aneuploidies correspond to complete chromosomal
aneuploidies of any one or more of the autosomes; the twenty-third
and twenty fourth chromosomal aneuploidy correspond to a complete
chromosomal aneuploidy of chromosomes X and Y. As aneuploidies can
comprise trisomies, tetrasomies, pentasomies and other polysomies,
and the number of complete chromosomal aneuploidies varies in
different diseases and in different stages of the same disease, the
number of complete chromosomal aneuploidies that are determined
according to the present method are at least 24, at least 25, at
least 26, at least 27, at least 28, at least 29, at least 30
complete, at least 40, at least 50, at least 60, at least 70, at
least 80, at least 90, at least 100 or more chromosomal
aneuploidies. Systematic karyotyping of tumors has revealed that
the chromosome number in cancer cells is highly variable, ranging
from hypodiploidy (considerably fewer than 46 chromosomes) to
tetraploidy and hypertetraploidy (up to 200 chromosomes) (Storchova
and Kuffer J Cell Sci 121:3859-3866 [2008]). In some embodiments,
the method comprises determining the presence or absence of up to
200 or more chromosomal aneuploidies in a sample form a patient
suspected or known to be suffering from cancer e.g. colon cancer.
The chromosomal aneuploidies include losses of one or more complete
chromosomes (hypodiploidies), gains of complete chromosomes
including trisomies, tetrasomies, pentasomies, and other
polysomies. Gains and/or losses of segments of chromosomes can also
be determined as described elsewhere herein. The method is
applicable to determining the presence or absence of different
aneuploidies in samples from patients suspected or known to be
suffering from any cancer as described elsewhere herein.
In some embodiments, any one of chromosomes 1-22, X and Y, can be
the chromosome of interest in determining the presence or absence
of any one or more different complete chromosomal aneuploidies in a
patient test sample as described above. In other embodiments, two
or more chromosomes of interest are selected from any two or more
of chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, X, or Y. In one embodiment, any one or
more chromosomes of interest are selected from chromosomes 1-22, X,
and Y comprise at least twenty chromosomes selected from
chromosomes 1-22, X, and Y, and wherein the presence or absence of
at least twenty different complete chromosomal aneuploidies is
determined. In other embodiments, any one or more chromosomes of
interest selected from chromosomes 1-22, X, and Y is all of
chromosomes 1-22, X, and Y, and wherein the presence or absence of
complete chromosomal aneuploidies of all of chromosomes 1-22, X,
and Y is determined. Complete different chromosomal aneuploidies
that can be determined include complete chromosomal monosomies of
any one or more of chromosomes 1-22, X and Y; complete chromosomal
trisomies of any one or more of chromosomes 1-22, X and Y; complete
chromosomal tetrasomies of any one or more of chromosomes 1-22, X
and Y; complete chromosomal pentasomies of any one or more of
chromosomes 1-22, X and Y; and other complete chromosomal
polysomies of any one or more of chromosomes 1-22, X and Y.
Determination of Partial Chromosomal Aneuploidies in Patient
Samples
In another embodiment, methods for determining the presence or
absence of any one or more different partial chromosomal
aneuploidies in a patient test sample comprising nucleic acid
molecules are provided. The steps of the method comprise (a)
obtaining sequence information for the patient nucleic acids in the
sample; and (b) using the sequence information to identify a number
of sequence tags for each of any one or more segments of any one or
more chromosomes of interest selected from chromosomes 1-22, X, and
Y and to identify a number of sequence tags for a normalizing
segment sequence for each of any one or more segments of any one or
more chromosomes of interest. The normalizing segment sequence can
be a single segment of a chromosome or it can be a group of
segments form one or more different chromosomes. The method further
uses in step (c) the number of sequence tags identified for each of
any one or more segments of any one or more chromosomes of interest
and the number of sequence tags identified for the normalizing
segment sequence to calculate a single segment dose for each of any
one or more segments of any one or more chromosome of interest; and
(d) comparing each of the single chromosome doses for each of any
one or more segments of any one or more chromosomes of interest to
a threshold value for each of said any one or more chromosomal
segments of any one or more chromosome of interest, and thereby
determining the presence or absence of one or more different
partial chromosomal aneuploidies in said sample.
In some embodiments, step (c) comprises calculating a single
segment dose for each of any one or more segments of any one or
more chromosomes of interest as the ratio of the number of sequence
tags identified for each of any one or more segments of any one or
more chromosomes of interest and the number of sequence tags
identified for the normalizing segment sequence for each of any one
or more segments of any one or more chromosomes of interest.
In other embodiments, step (c) comprises calculating a sequence tag
ratio for a segment of interest by relating the number of sequence
tags obtained for the segment of interest to the length of the
segment of interest, and relating the number of tags for the
corresponding normalizing segment sequence for the segment of
interest to the length of the normalizing segment sequence, and
calculating a segment dose for the segment of interest as a ratio
of the sequence tags density of the segment of interest and the
sequence tag density for the normalizing segment sequence. The
calculation is repeated for each of all chromosomes of interest.
Steps (a)-(d) can be repeated for test samples from different
patients.
A means for comparing segment doses of different sample sets is
provided by determining a normalized segment value (NSV), which
relates the segment dose in a test sample to the mean of the of the
corresponding segment dose in a set of qualified samples. The NSV
is calculated as:
.mu..sigma. ##EQU00009## where {circumflex over (.mu.)}.sub.j and
{circumflex over (.sigma.)}.sub.j are the estimated mean and
standard deviation, respectively, for the j-th segment dose in a
set of qualified samples, and x.sub.ij is the observed j-th segment
dose for test sample i.
In some embodiments, the presence or absence of one partial
chromosomal aneuploidy is determined. In other embodiments, the
presence or absence of two, three, four, five, six, seven, eight,
nine, ten, fifteen, twenty, twenty-five, or more partial
chromosomal aneuploidies are determined in a sample. In one
embodiment, one segment of interest selected from any one of
chromosomes 1-22, X, and Y is selected from chromosomes 1-22, X,
and Y. In another embodiment, two or more segments of interest
selected from chromosomes 1-22, X, and Y are selected from any two
or more of chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, X, or Y. In one embodiment, any
one or more segments of interest are selected from chromosomes
1-22, X, and Y comprise at least one, five, ten, 15, 20, 25, 50,
75, 100 or more segments selected from chromosomes 1-22, X, and Y,
and wherein the presence or absence of at least one, five, ten, 15,
20, 25, 50, 75, 100, or more different partial chromosomal
aneuploidies is determined. Different partial chromosomal
aneuploidies that can be determined include chromosomal
aneuploidies include partial duplications, partial multiplications,
partial insertions and partial deletions.
Samples that can be used for determining the presence or absence of
a chromosomal aneuploidy (partial or complete) in a patient can be
any of the biological samples described elsewhere herein. The type
of sample or samples that can be used for the determination of
aneuploidy in a patient will depend on the type of disease from
which the patient is known or suspected to be suffering. For
example, a stool sample can be chosen as a source of DNA to
determine the presence or absence of aneuploidies associated with
colorectal cancer. The method is also applicable to tissue samples
as described herein. Preferably, the sample is a biological sample
that is obtained by non-invasive means e.g. a plasma sample. As
described elsewhere herein, sequencing of the nucleic acids in the
patient sample can be performed using next generation sequencing
(NGS) as described elsewhere herein. In some embodiments,
sequencing is massively parallel sequencing using
sequencing-by-synthesis with reversible dye terminators. In other
embodiments, sequencing is sequencing-by-ligation. In yet other
embodiments, sequencing is single molecule sequencing. Optionally,
an amplification step is performed prior to sequencing.
In some embodiments, the presence or absence of an aneuploidy is
determined in a patient suspected to be suffering from a cancer as
described elsewhere herein e.g. lung, breast, kidney, head and
neck, ovary, cervix, colon, pancreas, esophagus, bladder and other
organs, and blood cancers. Blood cancers include cancers of the
bone marrow, blood, and lymphatic system, which includes lymph
nodes, lymphatic vessels, tonsils, thymus, spleen, and digestive
tract lymphoid tissue. Leukemia and myeloma, which start in the
bone marrow, and lymphoma, which starts in the lymphatic system,
are the most common types of blood cancer.
The determination of the presence or absence of one or more
chromosomal aneuploidies in a patient sample can be made without
limitation to determine the predisposition of the patient to a
particular cancer, to determine the presence or absence of a cancer
as part of routine screen in patients known and not known to be
predisposed to the cancer in question, to provide a prognosis for
the disease, to assess the need for adjuvant therapy, and to
determine the progress or regress of the diseases.
Genetic Counseling
Fetal chromosome abnormalities are a major contributor to
miscarriages, congenital anomalies, and perinatal deaths (Wellesley
et al. Europ. J. Human Genet., 20: 521-526 [2012]; Nagaoka et al.
Nature Rev. Genetics 13: 493-504 [2012]). Since the introduction of
amniocentesis, followed by the introduction of chorionic villus
sampling (CVS), pregnant women have had options to obtain
information about fetal chromosome status (ACOG Practice Bulletin
No. 77: Obstet Gynecol 109: 217-227 [2007]). Cytogenetic
karyotyping of fetal cells or chorionic villi obtained from these
procedures leads to diagnosis in the vast majority of cases with
very high sensitivity and specificity (.about.99%) when adequate
tissue is obtained (Hahnemann and Vejerslev, Prenat Diagn., 17:
801-820 1997; NICHD National Registry for Amniocentesis Study JAMA
236:1471-1476 [1976]). However, these procedures also pose risks to
the fetus and pregnant woman (Odibo et al. Obstet Gynecol 112:
813-819 [2008]; Odibo et al. Obstet Gynecol 111: 589-595
[2008]).
To mitigate these risks, a series of prenatal screening algorithms
have been developed to stratify women for their likelihood of the
most common fetal trisomies--T21 (Down syndrome) and trisomy 18
(T18, Edwards syndrome) and to a lesser extent trisomy 13 (T13,
Patau syndrome). The screens typically involve measurement of
multiple biochemical analytes in the maternal serum at different
time points combined with ultrasonographic measurement of the fetal
nuchal translucency (NT) and incorporation of other maternal
factors, such as age to generate a risk score. Based on their
development and refinement over the years and depending on when the
screening is administered (first or second trimester only,
sequential, or fully integrated) and how the screening is
administered (serum-only or serum combined with NT), a menu of
options has evolved with variable detection rates (65 to 90%) and
high screen positive rates (5%) (ACOG Practice Bulletin No. 77:
Obstet Gynecol 109: 217-227 [2007]).
For patients, following this multi-step process, the resultant
information or "risk score" can be confusing and anxiety provoking,
particularly in the absence of comprehensive counseling.
Ultimately, the results are weighed against the risks for
miscarriage from an invasive procedure in a woman's
decision-making. Better noninvasive means to obtain more definitive
information on fetal chromosomal status facilitates decision making
in this context. Such improved noninvasive means of obtaining more
definitive information on fetal chromosomal status are believed to
be provided by methods described herein.
In various embodiments, genetic counseling is contemplated as a
component of the use of the assays described herein, particularly
in a clinical context. Conversely, the aneuploidy detection methods
described herein can comprise one option offered in the context of
prenatal care and associated genetic counseling.
Accordingly, in various embodiments the methods described herein
can offered as a primary screen (e.g., for women with an a priori
pregnancy risk) or as a secondary screen to those women with a
positive "conventional" screen. In certain embodiments, it is
contemplated that the non-invasive prenatal testing (NIPT) methods
described herein additionally comprise a genetic counseling
component and/or that genetic counseling and pregnancy
"management", optionally, or definitively incorporated the NIPT
methods described herein.
For example, in certain embodiments, women present with one or more
a priori pregnancy risks. Such risks include, but are not limited
to one or more of the following:
1) Maternal age over 35 although it is noted that approximately 80%
of children born with Down's syndrome are born to women under the
age of 35.
2) Previous fetus/child with autosomal trisomy. It is believed that
the recurrence rate is about 1.6 to about 8.2 times the maternal
age risk depending on the type of trisomy, whether the previous
pregnancy was spontaneously aborted, and the maternal age at the
initial occurrence and the mother's age at subsequent prenatal
diagnosis.
3) Previous fetus/child with sex chromosome abnormality--not all
sex abnormalities have a maternal origin and not all have risk of
recurrence. When they do, the recurrence rate is about 1.6 to about
1.5 times the maternal age risk.
4) Parental carrier of chromosomal translocation.
5) Parental carrier of chromosome inversion.
6) Parental aneuploidy or mosaicism.
7) Use of certain assisted reproductive technologies.
In such circumstances, the mother, e.g., in consultation with a
physician, genetic counselor, and the like, may be offered use of
the methods described herein for non-invasive determination of the
presence or absence of a fetal aneuploidy (e.g., trisomy 21,
trisomy 18, trisomy 13, monosomy X etc.) subject to the various
considerations described below. In this regard, it is noted that
the methods described herein are believed to be effective even in
the first trimesters. Thus, in certain embodiments, use of the NIPT
methods described herein is contemplated as early as 8 weeks, and
in various embodiments at about 10 weeks or later.
In certain embodiments, the methods described herein can be offered
as a secondary screen to those women with a positive "conventional"
screen. For example, in certain embodiments, pregnant women may
present with a structural abnormality such as fetal cystic hygroma,
or increased nuchal translucency, e.g., as detected using
ultrasonography. Typically ultrasound for structural defects is
performed in weeks 18-22 and, particularly when an irregularity is
observed, may be coupled with a fetal echocardiogram. It is
contemplated that when an abnormality is observed (e.g., a positive
"conventional" screen), the mother, e.g., in consultation with a
physician, genetic counselor, and the like, may be offered use of
the methods described herein for non-invasive determination of the
presence or absence of a fetal aneuploidy (e.g., trisomy 21,
trisomy 18, trisomy 13, monosomy X etc.) subject to the various
considerations described below.
Thus, in various embodiments, genetic counseling is contemplated in
which the (NIPT) assays described herein are offered as a component
of prenatal care, the management of pregnancy and/or the
development/design of a birth plan. By offering NIPT as a secondary
screen to those women with a positive conventional screen (or other
a priori risk), the number of unnecessary amniocentesis and CVS
procedures are expected to decrease. However, the need for genetic
counseling will increase, as informed consent is an important
component of NIPT.
Since a positive NIPT result (using the methods described herein)
is more similar to a positive result from amniocentesis or CVS, in
genetic counseling women should be given the opportunity prior to
this testing to decide whether they desire this degree of
information. Pre-test genetic counseling for NIPT should also
include discussion/recommendation for confirmation of abnormal test
results via CVS, amniocentesis, cordocentesis, etc (depending upon
gestational age), so that appropriate consideration can be given to
the expected timing of results for post-test planning Per the
National Society of Genetic Counselors (NSGC, USA) statements on
the topic (see, e.g., Devers et al. Noninvasive Prenatal
Testing/Noninvasive Prenatal Diagnosis: the position of the
National Society of Genetic Counselors (by NSGC Public Policy
Committee). NSGC Position Statements 2012; Benn et al. Prenat
Diagn, 31: 519-522 [2011]), because NIPT does not presently screen
for all chromosomal or genetic conditions, it may not replace
standard risk assessment and prenatal diagnosis. It is contemplated
that patients with other factors (e.g., certain abnormal ultrasound
findings) suggestive of chromosome abnormality should receive
genetic counseling in which they are provided the option of
conventional confirmatory diagnostic testing, regardless of NIPT
results. In genetic counseling women should also be made aware that
for some patients an NIPT result may not be informative.
NIPT using the methods described herein is perhaps more similar to
CVS than amniocentesis in that detection of aneuploidy is typically
representative of the chromosomal constitution of the fetus, but in
some instances may be representative of confined placental
aneuploidy or confined placental mosaicism (CPM). CPM occurs in
approximately 1-2% of cases of CVS results today, and some women
undergo an amniocentesis at later gestational age after CVS to make
the distinction between apparently isolated placental aneuploidy
versus fetal aneuploidy. As NIPT is implemented more widely, cases
of CPM are expected to cause some number of positive NIPT results
that may not be subsequently confirmed by invasive procedure,
particularly amniocentesis. Again, in various embodiments, it is
contemplated that this information is presented to the patient in
the context of genetic counseling (e.g., by physician, genetic
counselor, etc.).
It will be recognized that in various embodiments, a component of
genetic counseling may be to recommend confirmatory diagnostics, to
inform regarding risk levels and timing for various confirmatory
diagnostics can to provide input as to the value of the information
provided by such confirmatory methods, particularly in the context
of the timing of the pregnancy. In various embodiments the genetic
counseling can also establish a plan to monitor the pregnancy
(e.g., follow-up ultrasound, additional physician visits, and the
like) and to set up a series of decision points where appropriate.
In addition, the genetic counseling can suggest and aid in
development of a birth plan that can include for example, decisions
regarding the site of delivery (e.g., home, hospital, specialized
facility, etc.), the staff involved at the site of delivery,
available tertiary care for the infant, and the like.
While the foregoing discussion focuses on the methods described
herein as a component (and perhaps secondary tool) in prenatal
diagnosis, as clinical experience accumulates and if results are
successful from comparative studies to conventional screening, it
is possible that the NIPT methods described herein can replace
current screening protocols and possibly serve as a primary
tool.
It is also contemplated that the methods described herein will find
use on pregnancies with multiple gestations.
Typically, it is expected that genetic counseling, e.g., as
described above, may be provided by a physician (e.g., primary
physician, obstetrician, etc.) and/or by a genetic counselor, or
other qualified medical professional. In certain embodiments the
counseling is provided face-to-face, however, it is recognized that
in certain instances, the counseling can be provided through remote
access (e.g., via text, cell phone, cell phone app, tablet app,
internet, and the like).
It is also recognized, that in certain embodiments, the genetic
counseling or a component thereof can be delivered by a computer
system. For example, "smart advice" systems can be provided that in
response to test results, instructions from a medical care
provider, and/or in response to queries (e.g., from a patient)
provide genetic counseling information (e.g., as described above).
In certain embodiments the information will be specific to clinical
information provided by the physician, healthcare system, and/or
patient. In certain embodiments the information can provided in an
iterative manner. Thus, for example, the patient can provide "what
if" inquiries and the system can return information such as
diagnostic options, risk factors, timing, and implication of
various outcomes.
In certain embodiments the information can be provided in a
transitory manner (e.g., presented on a computer screen). In
certain embodiments, the information can be provided in a
non-transitory manner. Thus, for example, the information can be
printed out (e.g., as a list of options and/or recommendations
optionally with associated timing, etc.) and/or stored on computer
readable media (e.g., magnetic media such as a local hard drive, a
server, etc., optical media, flash memory, and the like).
It will be appreciated that typically such systems will be
configured to provide adequate security such that patient privacy
is maintained, e.g., according to prevailing standards in the
industry.
The foregoing discussion of genetic counseling is intended to be
illustrative and not limiting. Genetic counseling is a
well-established branch of medical science and incorporation of a
counseling component with respect to the assays described herein is
within the scope and skill of the practitioner. Moreover, it is
recognized that as the field progresses, the nature of genetic
counseling and associated information and recommendations is likely
to alter.
Determination of Fetal Fraction
Methods of fetal fraction determination are disclosed in U.S.
patent application Ser. No. 12/958,347 filed Dec. 1, 2010, U.S.
patent application Ser. No. 13/365,240 filed Feb. 2, 2012, and U.S.
patent application Ser. No. 13/445,778 filed Apr. 12, 2012, which
are incorporated herein by reference in their entireties. A full
discussion of the techniques for determining fetal fraction can be
found in these documents.
The methods described herein enable determination of fetal fraction
in a sample comprising a mixture of fetal and maternal nucleic
acids, or more generally a mixture of nucleic acids having their
origin in two different genomes. For purposes of this discussion,
maternal and fetal nucleic acids will be described, but it should
be understood that any two genomes can be substituted therefore. In
some embodiments, fetal fraction is determined concurrently with
determining the presence or absence of a copy number varation such
as aneuploidy. As described more fully below, one set of tags of
from a test sample may be employed to determine both fetal fraction
and copy number variation.
Methods for quantifying fetal fraction rely on differences between
the fetal and the maternal genome. In certain embodiments described
herein, determination of fetal fraction of sample DNA relies on
multiple DNA sequence readings at sequence sites known to harbor
one or more polymorphisms. In some embodiments, the polymorphism
sites or target nucleic acid sequences are discovered while
aligning sequence tags to one another and/or a reference sequence.
In certain embodiments, the fetal fraction of sample DNA is
determined by considering copy number information for a particular
chromosome or chromosome sequence where there is a copy number
difference between the maternal chromosome and the fetal
chromosome. In such embodiments, the fetal fraction of sample DNA
is determined by considering the relative amounts of sample DNA
from the mother and fetus that originated with a chromosome or
segment determined or known to have a copy number variation. In
such embodiments, fetal fraction may be calculated using copy
number variations between maternal and fetal chromosomes. For this
purpose, the method and apparatus may calculate a normalized
chromosome value (NCV) as described below, or a similar metric.
Some methods are limited by the gender of the fetus, e.g., methods
for quantifying fetal fraction that rely on the presence of
sequences that are specific to the Y chromosome or determine the
chromosome dose of X chromosome for a male fetus. In some
embodiments, quantification of fetal DNA is directed toward fetal
targets that have that either have no maternal counterparts e.g. Y
chromosome sequences (Fan et al., Proc Natl Acad Sci
105:16266-16271 [2008] and US Patent Application Publication No.
2010/0112590, filed Nov. 6, 2009, Lo et al.) or the RHD1 gene in an
RhD-negative mother, or differ from the maternal background by at
multiple DNA base pairs. Other methods are independent of the
gender of the fetus, and rely on polymorphic differences between
the fetal and maternal genomes.
Allelic imbalances in polymorphisms can be detected and quantified
by various techniques. In some embodiments, digital PCR is used to
determine an allelic imbalance of polymorphisms e.g. a SNP on mRNA.
Alternatively, capillary gel electrophoresis is used to detect
differences in the size of the polymorphic region e.g. as in the
case of an STR.
In some embodiments, epigenetic differences can be detected e.g.
differential methylation of promoter regions, can be used alone or
in combination with digital PCR to determine differences between
the fetal and maternal genomes and quantity fetal fraction (Tong et
al., Clin Chem 56:90-98 [2010]). Modifications of epigenetic
methods are also included e.g. methylation-based DNA
discrimination, (Erich et al., AJOG 204: pages
205.e1-205.e11[2011]). In some embodiments, the fetal fraction is
estimated using sequencing of preselected panel(s) of polymorphic
sequences as described elsewhere herein.
Methods for quantifying fetal DNA in maternal plasma include
without limitation and in addition to the method of sequencing
panels of preselected polymorphic sequences as described elsewhere
herein, real-time qPCR, mass spectrometry, digital PCR including
microfluidic digital PCR, capillary gel electrophoresis.
The discussion in this section initially considers fetal fraction
as determined from one or more polymorphisms or other information
from chromosomes or chromosome segments that do not (or are
determined not to) have copy number variations. Fetal fraction
determined by such techniques will be referred to herein as non-CNV
fetal fraction or "NCNFF." Later in this section, techniques are
described for calculating fetal fraction from chromosomes or
chromosome segments determined to possess copy number variations.
Fetal fraction determined from such techniques will be referred to
herein as CNV fetal fraction or "CNFF."
In some embodiments, the fetal fraction is evaluated by determining
the relative contribution of a polymorphic allele derived from the
fetal genome and the contribution of the corresponding polymorphic
allele derived from the maternal genome. In some embodiments, the
fetal fraction is evaluated by determining the relative
contribution of a polymorphic allele derived from the fetal genome
to the total contribution of the corresponding polymorphic allele
derived from both the fetal and the maternal genome.
Polymorphisms can be indicative, informative, or both. Indicative
polymorphisms indicate the presence of fetal cell-free DNA
("cfDNA") in a maternal sample. Informative polymorphisms, such as
informative SNPs, yield information about the fetus--for example,
the presence or absence of a disease, genetic abnormality, or any
other biological information such as the stage of gestation or
gender. Informative polymorphisms in this instance are those which
identify differences between the sequence of the mother and the
fetus and are used in the methods disclosed herein. Stated another
way, informative polymorphisms are polymorphisms in a nucleic acid
sample that possess different sequences (i.e., they possess
different alleles) and the sequences are present in different
amounts. The different amounts of the sequences/alleles are used in
some of the methods herein to determine fetal fraction,
particularly NCNFF.
Polymorphic sites include, without limitation, single nucleotide
polymorphisms (SNPs), tandem SNPs, small-scale multi-base deletions
or insertions (IN-DELS or deletion insertion polymorphisms (DIPs)),
Multi-Nucleotide Polymorphisms (MNPs), Short Tandem Repeats (STRs),
restriction fragment length polymorphisms (RFLP), or any
polymorphisms possessing any other allelic variation of sequence in
a chromosome. In some embodiments, each target nucleic acid
comprises two tandem SNPs. The tandem SNPs are analyzed as a single
unit (e.g., as short haplotypes), and are provided herein as sets
of two SNPs.
In some embodiments, the fetal fraction is determined by
statistical and approximation techniques that evaluate the relative
contributions of zygosities from the fetal and maternal genomes by
using polymorphic sites to determine the relative contributions.
The fetal fraction can also be determined by electrophoresis
methods where certain types of polymorphic sites are
electrophoretically separated and used to identify relative
contribution of a polymorphic allele from the fetal genome and
relative contribution of the corresponding polymorphic allele from
the maternal genome.
In one embodiment shown in a process flow diagram in FIG. 6, fetal
fraction is determined by a method 600 of first obtaining a test
sample comprising a mixture of fetal and maternal nucleic acids in
operation 610, enriching the mixture of nucleic acids for
polymorphic target nucleic acids in operation 620, sequencing the
enriched mixture of nucleic acids in operation 630, and determining
the fetal fraction in the sample and aneuploidy simultaneously in
operation 640.
FIG. 7 shows a process flow diagram for some embodiments. Fetal
fraction is determined by: (i) obtaining a maternal plasma sample
in operation 710, (ii) purifying the cfDNA in the sample in
operation 720, (iii) amplifying the polymorphic nucleic acids in
operation 730, (iv) using massively parallel sequencing methods to
sequence the mixture in operation 740, and (v) calculating the
fetal fraction in operation 760. In another embodiment, fetal
fraction can be determined by (i) obtaining a maternal plasma
sample in operation 710, (ii) purifying the cfDNA in the sample in
operation 720, (iii) amplifying the polymorphic nucleic acids in
operation 730, (iv) separating the nucleic acids by size using
electrophoresis methods in operation 750, and (v) calculating the
fetal fraction in sample 770.
In one embodiment shown in process flow diagram in FIG. 8, the
fetal fraction is determined by: (i) obtaining a sample comprising
a mixture of fetal and maternal nucleic acids in operation 810,
(ii) amplifying the sample in operation 820, (iii) enriching the
sample by combining the amplified sample with unamplified sample
from the original mixture in operation 830, (iv) purifying the
sample in operation 840, and (v) sequencing the sample to determine
the fetal fraction using various methods in operation 850 to
determine the fetal fraction and the presence or absence of
aneuploidy simultaneously in operation 860.
In another embodiment shown in the process flow diagram in FIG. 9,
the fetal fraction is determined by: (i) obtaining a sample
comprising a mixture of fetal and maternal nucleic acids in
operation 910, (ii) purifying the sample in operation 920, (iii)
amplifying a portion of the sample in operation 930, (iv) enriching
the sample by combining the amplified sample with purified
unamplified portion of the original sample from the original
mixture in operation 940, and (v) sequencing the sample in
operation 950 to determine the fetal fraction and the presence or
absence of aneuploidy simultaneously in operation 960 using various
methods.
In another embodiment shown in the process flow diagram in FIG. 10,
the fetal fraction is determined by: (i) obtaining a sample
comprising a mixture of fetal and maternal nucleic acids in
operation 1010, (ii) purifying the sample in operation 1020, (iii)
amplifying a first portion of the sample in operation 1040, (iv)
preparing a sequencing library of the amplified portion of the
sample in operation 1050, (v) preparing a sequencing library of a
second purified unamplified portion of the sample in operation
1030, (vi) enriching the mixture by combining the two sequencing
libraries in operation 1060, and (vii) sequencing the mixture in
operation 1070 to determine the fetal fraction and the presence or
absence of aneuploidy simultaneously in operation 1080 using
various methods.
In another embodiment, the fetal fraction is determined by: (i)
obtaining a sample comprising a mixture of fetal and maternal
nucleic acids, (ii) purifying the sample, (iii) amplifying the
sample using labeled primers, and (iv) sequencing the sample using
electrophoresis to determine the fetal fraction using various
methods.
In another embodiment, the fetal fraction is determined by: (i)
obtaining a sample comprising a mixture of fetal and maternal
nucleic acids, (ii) purifying the sample, (iii) optionally
enriching the sample by amplifying a portion of the sample, and
(iv) sequencing the sample to determine the fetal fraction using
various methods.
Purification of the original obtained sample, amplified sample, or
amplified and enriched sample, or other nucleic acid samples
relevant to the methods disclosed herein (such as in operations
720, 840, 920, and 1020) can be completed by any conventional
technique. To separate cfDNA from cells, fractionation,
centrifugation (e.g., density gradient centrifugation),
DNA-specific precipitation, or high-throughput cell sorting, and/or
separation methods can be used. Optionally, the sample obtained can
be fragmented before purification or amplification. If the sample
used comprises cfDNA, then fragmentation may not be required
because cfDNA is fragmented in nature, with the fragments
frequently of size around 150 to 200 bp.
In some of the above-described processes, selective amplification
and enrichment is employed to increase the relative amount of
nucleic acid from regions where polymorphisms are located. A
similar result can be achieved by deep sequencing selected regions
of the genome, particularly regions where polymorphisms are
located.
Amplification
After obtaining a sample and purifying the sample, a portion of the
purified mixture of fetal and maternal nucleic acids (e.g. cfDNA)
is used to amplify a plurality of polymorphic target nucleic acids,
each comprising a polymorphic site. Amplification of the target
nucleic acids in the mixture of fetal and maternal nucleic acid is
accomplished in some implementations by any method that uses PCR
(polymerase chain reaction) or variations of the method, including
but not limited to asymmetric PCR, helicase-dependent
amplification, hot-start PCR, qPCR, solid phase PCR, and touchdown
PCR. In some embodiments, the sample can be partially amplified to
facilitate determining fetal fraction. In some embodiments,
amplification is not performed. The disclosed methods of
amplifications and other amplification techniques can be used in
operations 730, 820, 930, and 1040.
Amplification of SNPs
A number of nucleic acid primers are available to amplify DNA
fragments containing SNPs, and their sequences can be obtained, for
example, from databases known by one skilled in the art. Additional
primers can also be designed, for example, using a method similar
to that published by Vieux, E. F., Kwok, P-Y and Miller, R. D. in
BioTechniques (June 2002) Vol. 32, Supplement: "SNPs: Discovery of
Marker Disease," pp. 28-32.
Sequence-specific primers are selected to amplify target nucleic
acids. In one embodiment, target nucleic acids comprising a
polymorphic site are amplified as amplicons. In another embodiment,
target nucleic acids comprising two or more polymorphic sites, e.g.
two tandem SNPs, are amplified as amplicons. The single or tandem
SNPs are contained in amplified target nucleic acid amplicons of at
least about 100 bp. The primers used for amplifying the target
sequences comprising tandem SNPs are designed to encompass both SNP
sites.
Amplification of STRs
Some nucleic acid primers are available to amplify DNA fragments
containing STRs and such sequences can be obtained from databases
known by one skilled in the art.
In some embodiments, a portion of the mixture of fetal and maternal
nucleic acids is used as a template for amplifying target nucleic
acids that have at least one STR. A comprehensive listing of
references, facts and sequence information on STRs, published PCR
primers, common multiplex systems, and related population data are
compiled in STRBase, which may be accessed via the Internet at
cstl.nist.gov/strbase. Sequence information from GenBank.RTM. at
ncbi.nlm.nih.gov/genbank for commonly used STR loci is also
accessible through STRBase.
STR multiplex systems allow the simultaneous amplification of
multiple nonoverlapping loci in a single reaction, substantially
increasing throughput. Because of the high polymorphisms of STRs,
most individuals will be heterozygous. STRs can be used in
electrophoresis analysis as described further below.
Amplification can also be done using miniSTRs to generate
reduced-size amplicons to discern STR alleles that are shorter in
length. The method of the disclosed embodiments encompasses
determining the fraction of fetal nucleic acid in a maternal sample
that has been enriched with target nucleic acids each comprising
one miniSTR comprising quantifying at least one fetal and one
maternal allele at a polymorphic miniSTR, which can be amplified to
generate amplicons that are of lengths about the size of the
circulating fetal DNA fragments. Any one pair or a combination of
two or more pairs of miniSTR primers can be used to amplify at
least one miniSTR.
Enrichment
Samples that are enriched may include: a plasma fraction of a blood
sample; a sample of purified cfDNA that is extracted from plasma; a
sequencing library sample prepared from a purified mixture of fetal
and maternal nucleic acids; and others.
In certain embodiments, the sample comprising the mixture of DNA
molecules is non-specifically enriched for the whole genome prior
to whole genome sequencing i.e. whole genome amplification is
performed prior to sequencing. Non-specific enrichment of the
mixture of nucleic acids may refer to the whole genome
amplification of the genomic DNA fragments of the DNA sample that
can be used to increase the level of the sample DNA prior to
identifying polymorphisms by sequencing. Non-specific enrichment
can be the selective enrichment of one of the two genomes (fetal
and maternal) present in the sample.
In other embodiments, the cfDNA in the sample is enriched
specifically. Specific enrichment refers to the enrichment of a
genomic sample for specific sequences, e.g. polymorphic target
sequence, which is accomplished by methods that comprise
specifically amplifying target nucleic acid sequences that comprise
the polymorphic site.
In other embodiments, the mixture of nucleic acids present in the
sample is enriched for polymorphic target nucleic acids each
comprising a polymorphic site. Such enrichment can be used in
operation 620. Enrichment of a mixture of fetal and maternal
nucleic acids comprises amplifying target sequences from a portion
of nucleic acids contained in the original maternal sample, and
combining part or the entire amplified product with the remainder
of the original maternal sample, such as in operations 830 and
940.
In yet another embodiment, the sample that is enriched is a
sequencing library sample prepared from a purified mixture of fetal
and maternal nucleic acids. The amount of amplified product that is
used to enrich the original sample is selected to obtain sufficient
sequencing information for determining the fetal fraction. At least
about 3%, at least about 5%, at least about 7%, at least about 10%,
at least about 15%, at least about 20%, at least about 25%, at
least about 30% or more of the total number of sequence tags
obtained from sequencing are mapped to determine the fetal
fraction.
In one embodiment, in FIG. 10, enrichment includes amplifying the
target nucleic acids that are contained in a portion of an original
sample of a purified mixture of fetal and maternal nucleic acids
(e.g. cfDNA that has been purified from a maternal plasma sample)
in operation 1040. Similarly, the portion of purified unamplified
cfDNA is used to prepare a primary sequencing library in operation
1050. In operation 1060, a portion of the target library is
combined with the primary library generated from the unamplified
mixture of nucleic acids, and the mixture of fetal and maternal
nucleic acids comprised in the two libraries is sequenced in
operation 1070. The enriched library may include at least about 5%,
at least about 10%, at least about 15%, at least about 20%, or at
least about 25% of the target library. In operation 1080, the data
from the sequencing runs is analyzed and the simultaneous
determination of the fetal fraction and presence or absence of
aneuploidy is made as described in operation 640 of the embodiment
depicted in FIG. 6.
Sequence Technology
The enriched mixture of fetal and maternal nucleic acids is
sequenced. Sequence information that is needed for the
determination of fetal fraction can be obtained using any of the
known DNA sequencing methods, many of which are described elsewhere
herein. Such sequencing methods include next generation sequencing
(NGS), Sanger sequencing, Helicos True Single Molecule Sequencing
(tSMS.TM.), 454 sequencing (Roche), SOLiD technology (Applied
Biosystems), Single Molecule Real-Time (SMRT.TM.) sequencing
technology (Pacific Biosciences), nanopore sequencing,
chemical-sensitive field effect transistor (chemFET) array, Halcyon
Molecular's method that uses transmission electron microscopy
(TEM), ion torrent single molecule sequencing, sequencing by
hybridization, and others. In some embodiments, massively parallel
sequencing is adopted. In one embodiment, Illumina's
sequencing-by-synthesis and reversible terminator-based sequencing
chemistry is used. In some embodiments, partial sequencing is
used.
The sequenced DNA is mapped to a reference genome. Reference
genomes may be artificial or may be a human reference genome. Such
reference genomes include: artificial target sequences genome
comprising sequences of polymorphic target nucleic acids; an
artificial SNP reference genome; an artificial STR reference
genome; an artificial tandem-STR reference genome; the human
reference genome NCBI36/hg18 sequence, which is available on the
Internet at
genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105;
and the human reference genome NCBI36/hg18 sequence and an
artificial target sequences genome, which includes the target
polymorphic sequences e.g. a SNP genome. Some mismatch is allowed
during the mapping process.
In one embodiment, sequencing information obtained in operation 630
is analyzed and the simultaneous determination of fetal fraction
and determination of the presence or absence of aneuploidy is
made.
As explained above, a plurality of sequence tags are obtained per
sample. In some embodiments, at least about 3.times.106 sequence
tags, at least about 5.times.106 sequence tags, at least about
8.times.106 sequence tags, at least about 10.times.106 sequence
tags, at least about 15.times.106 sequence tags, at least about
20.times.106 sequence tags, at least about 30.times.106 sequence
tags, at least about 40.times.106 sequence tags, or at least about
50.times.106 sequence tags comprising between 20 and 40 bp reads
are obtained from mapping the reads to the reference genome per
sample. In one embodiment, all the sequence reads are mapped to all
regions of the reference genome. In one embodiment, the tags
comprising reads that have been mapped to all regions e.g. all
chromosomes, of the human reference genome are counted, and the
fetal aneuploidy i.e. the over- or under-representation of a
sequence of interest e.g. a chromosome or portion thereof, in the
mixed DNA sample is determined, and the tags comprising reads that
are mapped to the artificial target sequences genome are counted to
determine the fetal fraction. The method does not require
differentiation between the maternal and fetal genomes.
In one embodiment, the data from the sequencing runs is analyzed
and the simultaneous determination of the fetal fraction and
presence or absence of aneuploidy is made.
Sequencing Libraries
In some embodiments, a portion or all of the amplified polymorphic
sequences is used to prepare a sequencing library for sequencing in
a parallel fashion as described. In one embodiment, the library is
prepared for sequencing-by-synthesis using Illumina's reversible
terminator-based sequencing chemistry. A library can be prepared
from purified cfDNA and includes at least about 10%, at least about
15%, at least about 20%, at least about 25%, at least about 30%, at
least about 35%, at least about 40%, at least about 45%, or at
least about 50% amplified product.
Sequencing of the library generated by any one of the methods
depicted in FIG. 11 provides sequence tags derived from the
amplified target nucleic acids and tags derived from the original
unamplified maternal sample. Fetal fraction is calculated from the
number of tags mapped to an artificial reference genome.
Calculation of Fetal Fraction
As explained, after sequencing the relevant DNA, computational
methods can be used to map or align the sequence to a particular
gene, chromosome, allele, or other structure. A number of computer
algorithms exist to align sequences, including, without limitation,
BLAST (Altschul et al., 1990), BLITZ (MPsrch) (Sturrock &
Collins, 1993), FASTA (Pearson & Lipman, 1988), BOWTIE
(Langmead et al., Genome Biology 10:R25.1-R25.10 [2009]), or ELAND
(Illumina, Inc., San Diego, Calif., USA). In some embodiments, the
sequences of the bins are found in nucleic acid databases known to
those in the art, including, without limitation, GenBank, dbEST,
dbSTS, EMBL (the European Molecular Biology Laboratory), and the
DDBJ (the DNA Data Bank of Japan). BLAST or similar tools can be
used to search the identified sequences against the sequence
databases, and search hits can be used to sort the identified
sequences into the appropriate bins. Alternatively, a Bloom filter
or similar set membership tester may be employed to align reads to
reference genomes. See U.S. Patent Application No. 61/552,374 filed
Oct. 27, 2011 which is incorporated herein by reference in its
entirety.
As mentioned, the determination of the fetal fraction according to
some embodiments, particularly NCNFF techniques, is based on the
total number of tags that map to a first allele and the total
number of tags that map to a second allele at an informative
polymorphic site (e.g. a SNP) contained in a reference genome. The
informative polymorphic site is identified by the difference in the
allelic sequences and the amount of each of the possible alleles.
Fetal cfDNA is often present at a concentration that is <10% of
the maternal cfDNA. Thus, the presence of a minor contribution of
an allele to the mixture of fetal and maternal nucleic acids
relative to the major contribution of the maternal allele can be
assigned to the fetus. Alleles that are derived from the maternal
genome are herein referred to as major alleles, and alleles that
are derived from the fetal genome are herein referred to as minor
alleles. Alleles that are represented by similar levels of mapped
sequence tags represent maternal alleles. The results of an
exemplary multiplex amplification of target nucleic acids
comprising SNPs derived from a maternal plasma sample are shown in
FIG. 12.
Estimating Fetal Fraction Using Allele Ratios
The relative abundance of fetal cfDNA in the maternal sample can be
determined as a parameter of the total number of unique sequence
tags mapped to the target nucleic acid sequence on a reference
genome for each of the two alleles of the predetermined polymorphic
site. In one embodiment, the fraction of fetal nucleic acids in the
mixture of fetal and maternal nucleic acids is calculated for each
of the informative alleles (allele.sub.x) as follows:
.times..times..times..times..times..times.
.times..times..times..times..times..times..times..times..times..times..ti-
mes..times..times..times..times..times..times..times..times.
##EQU00010## and fetal fraction for the sample is calculated as the
average of the fetal fraction of all of the informative alleles.
Optionally, the fraction of fetal nucleic acids in the mixture of
fetal and maternal nucleic acids is calculated for each of the
informative alleles (allelex) as follows:
.times..times..times..times..times..times.
.times..times..times..times..times..times..times..times..times..times..ti-
mes..times..times..times..times..times..times..times..times..times.
##EQU00011## to compensate for the presence of two fetal alleles,
one being masked by the maternal background.
Estimating Fetal Fraction Using STR Sequences and Capillary
Electrophoresis
Individuals have different lengths of STRs due to different number
of repeats. Because of the high polymorphism of STRs, most
individuals will be heterozygous i.e. most people will possess two
alleles (versions)--one inherited from each parent--each with a
different number of repeats. The non-maternally inherited fetal STR
sequence will differ in the number of repeats from the maternal
sequence. Amplification of these STR sequences can result in one or
two major amplification products corresponding to the maternal
alleles (and the maternally inherited fetal allele) and one minor
product corresponding to the non-maternally inherited fetal allele.
When sequenced, the collected samples can be correlated with the
corresponding alleles and counted to determine relative fraction by
using Equation 3.
PCR is performed on a purified sample by using fluorescently
labeled primers. The PCR products comprising the STRs can be
separated and detected using manual, semi-automated or automated
electrophoresis methods. Semi-automated systems are gel-based and
combine electrophoresis, detection, and analysis into one unit. On
a semi-automated system, gel assembly and sample loading are still
manual processes; however, once samples are loaded onto the gel,
electrophoresis, detection and analysis proceed automatically. As
the name implies, capillary electrophoresis is carried out in a
microcapillary tube rather than between glass plates. Once samples,
gel polymer, and buffer are loaded onto the instrument, the
capillary is filled with gel polymer and the sample is loaded
automatically. Data collection occurs in "real time" as
fluorescently labeled fragments migrate past the detector at a
fixed point and can be viewed as they are collected. The sequence
obtained from capillary electrophoresis can be detected by a
program to measure the wavelengths of the fluorescent labels. The
calculation of fetal fraction is based on averaging all informative
markers. Informative markers are identified by the presence of
peaks on the electropherogram that fall within the parameters of
preset bins for the STRs that are analyzed.
The fraction of the minor allele for any given informative marker
is calculated by dividing the peak height of the minor component by
the sum of the peak height for the major component, and the
fraction is expressed as a percent for each informative locus
as
.times..times..times..times.
.times..times..times..times..times..times..times..times..function..times.-
.times..times..times..times..times..times..times..function..times..times..-
times. ##EQU00012##
The fetal fraction for a sample comprising two or more informative
STRs would be calculated as the average of the fetal fractions
calculated for the two or more informative markers.
Estimating Fetal Fraction Using Mixture Models
In embodiments disclosed herein, there are up to four different
data types (the zygosity cases) that make up the minor allele
frequency data for the polymorphisms under consideration.
As indicated in FIG. 13, cases 1 and 2 are the polymorphism cases
in which the mother is homozygous at a certain allele. In case 1,
if the baby and the mother are both homozygous, the polymorphism is
a case 1 polymorphism. This situation is typically not particularly
interesting because the collected data will only have presence of
one type of allele at the analyzed polymorphic site. In case 2, if
the mother is homozygous and the baby is heterozygous, the fetal
fraction, f, is nominally given by two times the ratio of the minor
allele count to the coverage. Coverage is defined as the total
number of reads or tags (both fetal and maternal) mapping to a
particular site of a polymorphism. The equation for approximating
the fetal fraction as a fraction of the fetal and maternal sample
for case 2 is as follows:
.times..times..times..times..times..times..times..times..times..times..ti-
mes..times..times..times..times..times..times..times..times..times.
##EQU00013##
In case 3, where the mother is heterozygous and the baby is
homozygous, the fetal fraction is nominally one minus two times the
ratio of the minor allele count to the coverage. The equation for
approximating fetal fraction as a fraction of the total number of
reads in both the fetal and maternal sample in case 3 is as
follows:
.times..times..times..times..times..times..times..times..times..times..ti-
mes..times..times..times..times..times..times..times..times..times.
##EQU00014##
Finally, in case 4 where both the mother and the fetus are
heterozygous, the minor allele fraction should always be 0.5,
barring error. The fetal fraction cannot be derived for
polymorphisms falling into case 4.
Table 7 summarizes an example of estimating fetal fraction using
Equations 4 and 5 if the number of reads of the major allele is 300
and the number of reads of the minor allele is 200. The coverage
would be 500.
TABLE-US-00007 TABLE 7 Example of estimating fetal fraction using
zygosity Case Mom Baby Example 1 Homozygous Homozygous Cannot tell
2 Homozygous Heterozygous .times. ##EQU00015## 3 Heterozygous
Homozygous .times. ##EQU00016## 4 Heterozygous Heterozygous 0.5 if
coverage = 500, total number of reads: allele.sub.B = 300 (major),
allele.sub.A = 200 (minor) *This calculation of fetal fraction is
for equation illustration purposes only and is not representative
of actual fetal fraction values obtained from the methods in the
disclosed embodiments.
In certain embodiments, a mixture model may be employed to classify
a collection of polymorphisms into two or more of the presented
zygosity cases and concurrently estimate the fetal DNA fraction
from mean allele frequencies for each of these cases. Generally, a
mixture model assumes that a particular collection of data is made
up of a mixture of different types of data, each of which has its
own expected distribution (e.g., a normal distribution). The
process attempts to find the mean and possibly other
characteristics for each type of data. In embodiments disclosed
herein, there are up to four different data types (the zygosity
cases) that make up the minor allele frequency data for the
polymorphisms under consideration.
In certain embodiments employing mixture models, one or more
factorial moments given by Equation 10 are calculated for the
positions where polymorphisms are being considered. For example, a
factorial moment F.sub.i (or a collection of factorial moments) is
calculated using multiple SNP positions considered in the DNA
sequence. As shown in Equation 10 below, each of the various
factorial moments F.sub.i is a summation over all the various
polymorphism positions under consideration for the ratio of minor
allele frequency a.sub.i to coverage d.sub.i for a given position.
As shown in Equation 11 below, these factorial moments are also
related to the parameters .alpha. and p.sub.i associated with each
of the four zygosity cases described above. Specifically, they
related to the probability p.sub.i for each of the cases as well as
the relative amounts of each of the four cases in the collection of
polymorphisms under consideration given by .alpha.. As explained,
the probability p.sub.i is a function of the fraction of fetal DNA
in the cell-free DNA in the mother's blood. As explained more fully
below, by calculating a sufficient number of these factorial
moments, the method provides a sufficient number of expressions to
solve for all the unknowns. The unknowns in this case would be the
relative amounts of each of the four cases in the population of
polymorphisms under consideration as well as the probabilities (and
hence fetal DNA fractions) associated with each of these four
cases. Similar results can be obtained using other versions of
mixture models. Some versions make use of only polymorphisms
falling into cases 1 and 2, with polymorphisms for cases 3 and 4
being filtered by a thresholding technique.
Thus, the factorial moments may be used as part of a mixture model
to identify the probabilities of any combination of the four cases
of zygosity. And, as mentioned, these probabilities, or at least
those for cases 2 and 3, are directly related to the fraction of
fetal DNA in the total cell-free DNA in the mother's blood.
It should also be mentioned that sequencing error given by e may be
employed to reduce the complexity of the system of factorial moment
equations that must be solved. In this regard, it should be
recognized that the sequencing error actually can have any one of
four results (corresponding to each of the four possible bases at
any given polymorphism position).
Let the major allele count at genomic position j be B, the first
order statistic of counts (number of reads counted) at position j.
The major allele, b, is the corresponding arg max. Subscripts are
used when more than one SNP is being considered. The major allele
count is given by:
B.ident.B.sub.i.ident.{b.sub.j}.ident.w.sub.j,i.sup.(1)=max.sub.i.epsilon-
.{1,1,3,4}{w.sub.j,i} Equation 6
Let the minor allele count at position j be A, the second order
statistic of counts (i.e. the second highest allele count) at
position j: A.ident.A.sub.i.ident.{a.sub.j}=w.sub.j,i.sup.(2)
Equation 7
Coverage is defined as the total number of reads (both fetal and
maternal) mapping to a particular site of a polymorphism. Let
coverage at position j be defined as D:
D.ident..sub.j={d.sub.i}=A.sub.j+B.sub.j Equation 8
In this embodiment, the minor allele frequency A is a sum of four
terms as shown in Equation 9. The four heterozygosity cases
described suggest the following binomial mixture model for the
distribution of a.sub.i minor allele counts in points
(a.sub.i,d.sub.i) where d.sub.i is the coverage:
A={a.sub.i}.about..alpha..sub.1Bin(p.sub.1,d.sub.i)+.alpha..sub-
.2Bin(p.sub.2,d.sub.i)+.alpha..sub.3Bin(p.sub.3,d.sub.i)+.alpha..sub.4Bin(-
p.sub.4,d.sub.i) Equation 9
where
1=.alpha..sub.1+.alpha..sub.2+.alpha..sub.3+.alpha..sub.4
m=4
Each term corresponds to one of the four zygosity cases. Each term
is the product of a polymorphism fraction .alpha. and a binomial
distribution of the minor allele frequency. The .alpha.s represent
the fraction of the polymorphisms falling into each of the four
cases. Each binominal distribution has an associated probability,
p, and coverage, d. The minor allele probability for case 2, for
example, is given by f/2 where f is the fetal fraction. Various
models for relating p.sub.i to fetal fraction and sequencing error
rates are described below. The parameters .alpha..sub.i relate to
population specific parameters and the ability to let these values
"float" gives these methods additional robustness with respect to
factors like ethnicity and progeny of the parents.
The disclosed embodiments make use of factorial moments for the
allele frequency data under consideration. As is well known, a
distribution's mean is the first moment. It is the expected value
of the minor allele frequency. The variance is the second moment.
It is calculated from the expectation value of the allele frequency
squared.
For various heterozygosity cases, Equation 9 above can be solved
for fetal fraction. In certain embodiments, fetal fraction is
solved through the method of factorial moments in which the mixture
parameters can be expressed in terms of moments that can easily be
estimated from the observed data.
The allele frequency data across all polymorphisms may be used to
calculate i-th factorial moment F.sub.i (a first factorial moment
F.sub.1, a second factorial moment F.sub.2, etc.) as shown in
Equation 10. (SNPs are used for purposes of example only. Other
types of polymorphisms may be used as discussed elsewhere herein.)
Given n SNP positions, the factorial moments are defined as
follows:
.times..times..times..times..times..times..function..function..times..tim-
es..times..times..times..times..function..times..times..times..times..func-
tion..times..times..times. ##EQU00017##
As indicated by these equations, the factorial moments are
summations of terms over i, the individual polymorphisms in the
data set, where there are n such polymorphisms in the data set. The
terms being summed are functions of the minor allele counts
a.sub.i, and coverage values d.sub.i.
Usefully, the factorial moments have relationships with the values
of .alpha..sub.i and p.sub.i as illustrated in Equation 11.
Factorial moments can be related to the {.alpha..sub.i,p.sub.i}
such that
.apprxeq..times..alpha..times..times..times..apprxeq..times..alpha..times-
..times..times..times..times..apprxeq..times..alpha..times..times..times..-
times..times..apprxeq..times..alpha..times..times..times.
##EQU00018##
From the probabilities p.sub.i, one can determine the fetal
fraction, f. For example,
.times..times. ##EQU00019## Thus, the responsible logic can solve a
system of equations relating the unknown .alpha. and p variables to
the factorial moment expressions for minor allele fractions across
the multiple polymorphisms under consideration. Of course, there
are other techniques for solving the mixture models within the
scope of the disclosed embodiments.
A solution can be identified by solving for the
{.alpha..sub.i,p.sub.i} in a system of equations derived from the
above relation Equation 8 when n>2* (number of parameters to be
estimated). Obviously, the problem becomes much more difficult
mathematically for higher g as more {.alpha..sub.i,p.sub.i} need to
be estimated.
It is typically not possible to accurately discriminate between
case 1 and 2 (or case 3 and 4) data by simple thresholds at lower
fetal fractions. Case 1 and 2 data is easily separated from case 3
and 4 data by discriminating at point
.times. ##EQU00020## where A is the minor allele count and D is the
coverage and T is the threshold. Use of T=0.5 has been found to
perform satisfactorily.
Note that the mixture model method employing Equations 10 and 11
makes use of the data for all polymorphisms but does not separately
account for the sequencing error. Appropriate methods that separate
data for the first and second cases from data for the third and
fourth cases can account for sequencing error.
In further examples, the data set provided to a mixture model
contains data for only case 1 and case 2 polymorphisms. These are
polymorphisms for which the mother is homozygous. A threshold
technique may be employed to remove the case 3 and 4 polymorphisms.
For example, polymorphisms with minor allele frequencies greater
than a particular threshold are eliminated before employing the
mixture model. Using appropriately filtered data and factorial
moments as reduced to Equations 13 and 14 below, one may calculate
the fetal fraction, f, as shown in Equation 15. Note that Equation
13 is a restatement of Equation 9 for this implementation of a
mixture model. Note also that in this particular example, the
sequencing error associated with the machine reading is not known.
As a consequence, the system of equations must separately be solved
for the error, e.
FIG. 14 shows a comparison of the results using this mixture model
and the known fetal fraction (x-axis) and estimated fetal fraction
(y-axis). If the mixture model perfectly predicted the fetal
fraction, the plotted results would follow the dashed line.
Nevertheless, the estimated fractions are remarkably good,
particularly considering that much of the data was eliminated prior
to applying the mixture model.
To further elaborate, several other methods are available for
parameter estimation of the model from Equation 7. In some cases, a
tractable solution can be found by setting derivatives to zero of
the chi-squared statistic. In cases where no easy solution can be
found by direct differentiation, Taylor series expansion of the
binomial probability distribution function (PDF) or other
approximating polynomials can be effective. Minimum chi-square
estimators are well-known to be efficient. The method of moments
solutions from Equation 9 can be used as a starting point for the
iteration. The following chi-square estimator can be used
.chi..function..alpha..times..times..alpha..times..function..function..ti-
mes..times. ##EQU00021## where P.sub.i is the number of points of
count i. An alternative method from Le Cam ["On the Asymptotic
Theory of Estimation and Testing Hypotheses," Proceedings of the
Third Berkeley Symposium on Mathematical Statistics and
Probability, Vol. 1 Berkeley Calif.: University of CA Press, 1956,
pp. 129-156] uses Ralph-Newton iteration of the likelihood
function.
In accordance with another application, a method of resolving
mixture models involving expectation maximization methods operating
on mixtures of approximating Beta distributions is discussed.
Model 1: Cases 1 and 2, Sequencing Error Unknown
Consider a reduced model that only accounts for heterozygosity
cases 1 and 2. In this case the mixture distribution can be written
as
A={a.sub.i}.about..alpha..sub.1Bin(e,d.sub.i)+.alpha..sub.2Bin(f/2,d.sub.-
i) Equation 13
where
1=.alpha..sub.1+.alpha..sub.2
m=4
And the system F.sub.1=.alpha..sub.1e+(1-.alpha..sub.1)(f/2)
F.sub.2=.alpha..sub.1e.sup.2+(1-.alpha..sub.1)(f/2).sup.2
F.sub.3=.alpha..sub.1e.sup.3+(1-.alpha..sub.1)(f/2).sup.3 Equation
14 is solved for e (sequencing error rate), .alpha. (proportion of
case 1 points), and f (fetal fraction), where the F.sub.i are
defined as in Equation 10 above. A closed form solution for fetal
fraction is chosen to be the real solution of
.apprxeq..times..+-..times..times..times..function..times..times..times..-
times..times. ##EQU00022## that is between 0 and 1.
To gauge the performance of estimators, a simulated data-set of
Hardy-Weinberg Equilibrium points (a.sub.i,d.sub.i) was constructed
with fetal fraction designed to be {1%, 3%, 5%, 10%, 15%, 20%, and
25%} and a constant sequencing error rate of 1%. The 1% error rate
is the currently accepted rate for the sequencing machines and
protocols being used and is consistent with the graph of Illumina
Genome analyzer II data shown in FIG. 15. Equation 15 was applied
to the data and found, with the exception of a four point bias
upwards, general agreement with the "known" fetal fraction.
Interestingly, the sequencing error rate, e, is estimated to be
just above 1%.
Model 2: Cases 1 and 2, Sequencing Error Known
In the next mixture model example, thresholding or another
filtering technique is again employed to remove data for
polymorphisms falling into cases 3 and 4. However in this case, the
sequencing error is known. This simplifies the resulting expression
for fetal fraction, f, as shown in Equation 16. FIG. 16 shows that
this version of a mixture model provided improved results compared
to the approach employed with Equation 15. Let the sequencing
machine error rate be e in the subsequent equations.
A similar approach is shown in Equations 17 and 18. This approach
recognizes that only some sequencing errors add to the minor allele
count. Rather, only one in every four sequencing errors should
increase the minor allele count. FIG. 17 shows remarkably good
agreement between the actual and estimated fetal fractions using
this technique.
Since the sequencing error rate of the machines used is known to a
great extent, the bias and complexity of calculations can be
reduced by eliminating e as a variable to be solved. Thus we obtain
the system of equations
F.sub.1=.alpha..sub.1e+(1-.alpha..sub.1)(f/2)
F.sub.2=.alpha..sub.1e.sup.2+(1-.alpha..sub.1)(f/2).sup.2 Equation
16 for fetal fraction f to obtain the solution:
.apprxeq..times. ##EQU00023## FIG. 16 shows that using the machine
error rate as a known parameter reduces the upward bias by a
point.
Model 3: Cases 1 and 2, Sequencing Error Known, Improved Error
Models
To ameliorate bias in the model, we expanded the error model of the
above equations to account for the fact that not every sequencing
error event will add to minor allele count A=a.sub.i in
heterozygosity case 1. Furthermore, we allow for the fact that
sequencing error events may contribute to heterozygosity case 2
counts. Hence we determine fetal fraction f by solving for the
following system of factorial moment relations:
.alpha..times..alpha..times..times..times..alpha..function..alpha..times.-
.times..times. ##EQU00024## The solution to the system is then:
.apprxeq..times..times..times..times..times..times.
##EQU00025##
FIG. 17 shows that simulated data using the machine error rate as a
known parameter, enhancing the case 1 and 2 error models, greatly
reduces the upward bias to less than a point for fetal fraction
below 0.2.
Using Fetal Fraction to Classify Affected Samples
In certain embodiments, fetal fraction estimates are employed to
further characterize affected samples. In some cases, fetal
fraction estimates allow an affected sample to be classified as a
mosaic, a complete aneuploidy or a partial aneuploidy. One
computer-implemented approach to obtaining this information is
depicted with respect to the flowchart of FIG. 18A. This and
related methods may be performed to provide simultaneous estimation
of fetal fraction, determination of CNVs and classification of the
CNVs. In other words, the same tags may be employed to perform any
of three of these functions.
In order to use this method, two modes of estimating fetal fraction
are employed. One mode produces a NCNFF value and the other mode
produces a CNFF value. As explained, the CNFF value is obtained
using a technique that relies on a chromosome or chromosome segment
determined to possess a copy number variation. It need not rely on
polymorphisms to calculate fetal fraction. An example of a
non-polymorphic technique to calculate fetal fraction is described
below in Example 17, which assumes that there is a duplication or
deletion of a full chromosome and employs the following expression:
ff.sub.(i)=2*NCV.sub.jACV.sub.jU Equation 25 where j represents the
identify of an aneuploidy chromosome and CV represents the
coefficient of variation obtained from the qualified samples used
to determine the mean and standard deviation in the expression for
NCV.
The NCNFF value is obtained using a technique that relies on a
chromosome or chromosome segment that does not have a copy number
variation. Stated another way, the NCN fetal fraction is determined
by a technique that reliably determines fetal fraction assuming
normal ploidy of the portion of the genome used to calculate fetal
fraction. The CN fetal fraction is determined by a technique that
assumes the sample under consideration has a form of aneuploidy.
The CNV of the affected chromosome or chromosome segment is used to
calculate the CN fetal fraction. Techniques for its calculation are
presented below.
By comparing the estimated value of NCN fetal fraction against the
estimated value of CN fetal fraction, a method can determine the
type of aneuploidy that may be present in a sample. Basically, if
the NCN fetal fraction and the CN fetal fraction values match, the
ploidy assumption in the techniques for estimating CN fetal
fraction can be considered to be true. For example, if the method
of calculating CN fetal fraction assumes that the sample has a
complete chromosomal aneuploidy exhibiting either a single
additional copy of a chromosome or a single deletion of a
chromosome, and the NCN fetal fraction value matches the CN fetal
fraction value, then the method may conclude that the sample
exhibits a complete chromosomal aneuploidy. The basis for making
the assumption is described in more detail below.
The NCN fetal fraction may be determined by various techniques. In
some embodiments, the NCN fetal fraction is estimated using
selected polymorphisms in a reference genome. Examples of these
techniques were described above. In other embodiments, NCN fetal
fraction is determined using the relative amount of X chromosome or
Y chromosome (e.g., the chromosome dose of such chromosome) from a
sample containing DNA from a pregnant mother carrying a son. The
son's genome will not include a second copy of the X chromosome.
Knowing this, the relative amount of X chromosome DNA can be used
to provide a NCN value of fetal fraction.
Turning to the flowchart 1800 of FIG. 18A, a NCN fetal fraction
estimate 1802 and a CN fetal fraction estimate 1804 are compared.
If they match as indicated at block 1806 the process is concluded
and it is determined that the assumption implicit in the technique
for estimating CN fetal fraction is true. In various embodiments,
this assumption is that a trisomy or monosomy is present in one of
the chromosomes of the fetus.
If, on the other hand, the comparison indicates that the two values
of fetal fraction do not match (condition 1808) and in fact the
estimation of CN fetal fraction is less than the NCN fetal
fraction, then a second phase of the method is executed as
indicated at block 1810.
In this second phase, the method determines whether the sample
contains a partial aneuploidy or a mosaic. Further, if the sample
includes a partial aneuploidy, the method determines where on the
aneuploid chromosome the aneuploidy resides. In certain
embodiments, this is accomplished by first binning the affected
chromosome into multiple blocks. In one example, each block is
about 1 million base pairs in length. Of course, other block
lengths may be used such as about 1 kb, about 10 kb, about 100 kb,
etc. The blocks do not overlap and span much or all of the length
of the chromosome. The blocks or bins are compared to one another
and this comparison provides insight about the condition. In one
approach, for each block or bin, the mapped tags are counted and
optionally converted to bin doses. These counts or bin doses will
indicate which, if any of the bins or blocks is aneuploid. As part
of the analysis of individual bins, it may be appropriate to
normalize the information from each bin to account for inter-bin
variations such as G-C content. The resulting normalized bins may
be referred to as NBV for normalizing bin values; NBV is an example
of a chromosome segment that is normalized to tags mapped to
normalizing segments of GC content of segments with similar GC
content (as in Example 19 below). In some embodiments, the fetal
fraction is calculated for each bin and the individual values of
fetal fraction values are compared. This sequential analysis of
each bin is depicted in block 1812 of FIG. 18A. If any of the bins
or blocks is identified as having aneuploidy (by considering tag
densities, fetal fractions or other information), the method
determines that the sample comprises a partial aneuploidy and
additionally localizes the aneuploidy with the bin in which the tag
count sufficiently deviates from an expected value. See block
1814.
If, however, when analyzing the individual the ends of the
chromosome under consideration, the method does not identify any
region of the chromosome exhibiting aneuploidy, the method
determines that the sample contains a mosaic. See block 1816.
In related embodiments, copy number variations in portions of a
genome are identified and classified in a process employing two
independent calculations of fetal fraction, one from a portion of a
genome preliminarily determined to harbor a copy number variation
and another from elsewhere on the genome. An example of such
process is depicted in the flow chart of FIGS. 18B and 18C.
Turning to the flow chart shown in FIG. 18B, the depicted process
begins at operation 1803 where a nucleic acid sample under
consideration is sequenced and aligned to a reference genome
sequence. As explained, the reference genome can be any one of many
different nucleic acid sequences chosen for the sample under
analysis. In a particular example, the reference genome is the
human genome of a single individual or a consensus sequence of
multiple individuals. In some examples, the reference genome is
believed not to possess an aneuploidy or other significant copy
number variation.
Sequencing can be accomplished by any one or more of various
techniques. The chosen technique should provide a measure of the
relative quantity or amount of all relevant portions of the
reference genome present in the sample. In a particular embodiment,
the sequencing technique is a massively parallel sequencing
technique such as one of is described elsewhere herein. In some
embodiments, massively parallel sequencing is performed on cell
free DNA (cfDNA) obtained/extracted from the blood of a pregnant
female. Of course, other samples containing a nucleic acid of
interest may be employed. Generally, the sample is believed to
contain nucleic acid originating from at least two distinct genomes
(e.g. maternal and fetal genomes).
As depicted in FIG. 18B, the process employs multiple iterations,
one for each of many "bins" within the reference genome. See item
1805 in the figure. In some cases, the reference genome is divided
into bins of equal size, e.g. about 1 Mb in length. In other
examples, the bins are of unequal size. In either case, each bin is
separately considered to determine whether its sequence contains a
copy number variation. In some embodiments, the bin boundaries are
defined prior to alignment. In other embodiments, the bins are
defined only after sequences from the sample are aligned to the
reference genome. In some cases, bins are defined so that each bin
contains an approximately equal amount of nucleic acid from the
sample, in which case the bin boundaries are not defined until
after alignment. The bin size is chosen based on the sequencing
depth and other considerations, with sizes ranging from whole
chromosomes (or even groups of chromosomes) to small segments
(e.g., 100 kb bins or even as small as 1 kb). Small bin sizes may
be appropriate when employing deep sequencing (e.g., more than 2
billion tags generated from sequencing).
Each iteration of the process described herein considers the
sequences aligned to a single bin of the reference genome. As
explained in more detail, each iteration determines not only
whether the bin harbors a copy number variation but also the fetal
fraction of the sample nucleic acid aligning to the bin.
It should be understood that while the process of FIG. 18B is
described with reference to "fetal fraction," the concept of fetal
fraction extends to fractions of any two distinct genomes contained
in a sample. For example, if a sample contains DNA originating from
somatic and cancerous cells, the fraction of nucleic acid in the
sample originating from the cancer cells can be determined in a
like manner as employed to determine the fetal fraction of DNA
taken from the blood or other sample the pregnant mother.
As depicted in FIG. 18B, operations conducted in a single iteration
for the bin under consideration involve counting sequence tags or
otherwise determining the quantity of the nucleic acid present in
the bin. See operation 1807. In the case of a massively parallel
sequencing technique that produces reads and tags of a defined
length (e.g., about 20 or 25 or 30 or 36 base pairs in length), the
number of such tags aligning to the bin in question is simply
counted. The count represents a quantity or mass of nucleic acid
originating from the all genomes in the sample corresponding to the
bin under consideration.
The quantity of sequenced nucleic acid determined to be present in
the current bin is optionally normalized to account for biases that
may have been introduced in the sample by collecting, sequencing,
aligning, or other processing employed to determine the amount of
nucleic acid in the current bin. See operation 1809. In some
examples, normalization is performed on the basis of the relative
amount of guanine and cytosine in the bin under consideration ("GC"
content). See for example, U.S. patent application Ser. No.
13/009,708, filed Jan. 19, 2011, which is incorporated herein by
reference in its entirety. GC content may be determined from the
reference sequence or from the sample sequences themselves. In some
cases, the quantity of a sample in a bin is normalized using the
quantity of sample in one or more other bins determined to have GC
contents similar to that of the bin under consideration. The BRVs
described in the Example 20 are calculated in this manner. In
certain embodiments, the reference sequence is initially divided
into bins and each bin is ranked on the basis of its GC content so
that bins having similar GC content can be immediately recognized
for use in normalizing. In one embodiment, a bin ratio value is
given by the number of tags in bin under consideration divided by
the sum of numbers of tags in 10 other bins chosen to have the
closest GC content to the bin under consideration.
More generally, the normalization can be conducted on the basis of
empirically determined information or on the basis of a priori
considerations. As an example, an empirical normalization process
may employ the detected amount of one or more normalizing
chromosomes or portions of chromosomes measured in the sample. Such
normalizing chromosomes or portions may be identified
experimentally to provide good discrimination of copy number. Such
normalizing chromosomes or portions may be identified from
qualified samples as described elsewhere herein. In some cases, the
basis of normalization is a semi empirical consideration.
Returning to the flow chart of FIG. 18B, in an operation 1810 the
quantity of nucleic acid in the current bin is transformed to
facilitate a statistically relevant discrimination of samples
possessing a genome with a copy number variation from samples which
do not possess a copy number variation at the bin under
consideration. In some cases, the copy number variation of interest
exists only in the genome present in a low fraction (e.g., the
fetal genome in a mixture of maternal and fetal DNA). Hence,
operation 1810 may be employed to facilitate identification of copy
number variation in samples where the variation exists only in a
small fraction of the total nucleic acid in the sample. Generally,
the value produced in operation 1810 is calculated from a
difference between the normalized value of the quantity of nucleic
acid and an expected value of the quantity for a bin having a known
copy number. In one example, the statistically relevant form
compares the quantity of nucleic acid identified in operations 1807
and/or 1809 with a median quantity of nucleic acid obtained from
samples having normal copy numbers in the bin under consideration.
In some embodiments, the difference in these quantities is scaled
by a factor such as the median absolute deviation in the samples
used to generate the median value. In one example, the amount of
nucleic acid provided in operation 1810 takes the form of a
z-score. Other forms include a t-test, log ratios, and related
representations, any of which may be suggested by the distribution
found in the sample.
Next, as illustrated at operation 1813, the method determines
whether the amount of nucleic acid in the bin under consideration
suggests a copy number variation for the bin under consideration.
This maybe accomplished by simply determining whether the value
calculated in operation 1810 is greater than or less than a certain
magnitude associated with a normal copy number (e.g., 4 in the case
of a z-score produced as described). If the evaluation conducted at
operation 1813 indicates that the quantity of nucleic acid from the
sample in the bin under consideration does not represent a copy
number variation, then there is no reason to further consider the
current bin. See block 1818 of the flowchart. From this juncture,
the process typically returns to operation 1805 where the next bin
in the reference genome is considered.
Assuming however that the evaluation conducted in operation 1813
indicates that the bin under consideration harbors a copy number
variation, the process undertakes an operation 1815 which
calculates a value of fetal fraction using the quantity of
sequenced nucleic acid present in the current bin. Various
techniques for calculating fetal fraction are suitable for use with
this process. In some cases, the technique assumes that the copy
number variation within the bin is of a particular type. For
example, a copy number variation may be assumed to be a complete
duplication or a complete deletion of the entire nucleic acid
sequence contained in the current bin. In such case, the fetal
fraction may be calculated according to equation 31 in the Example
20 (assuming that a z-score is used to provide a statistically
meaningful representation of nucleic acid quantity). For example,
the fetal fraction may be calculated as the absolute value of two
times the z-score for the bin under consideration times the
coefficient of variation. The coefficient of variation is obtained
from the external samples used to calculate the median value of BRV
and median absolute deviation for the bin under consideration.
If the calculated value of fetal fraction is less than a defined
threshold (as indicated by the decision operation 1817), the
process deems the information about the nucleic acid in the current
bin unreliable and does not further consider the sequence in the
current bin. In this case, the process continues through operation
1818 as described above. In one example, bins having a calculated
fetal fraction value of less than about 4% are disregarded.
If the calculated value of fetal fraction determined in operation
1815 is greater than the defined threshold, the information about
the bin is considered sufficiently reliable to continue with the
evaluation. In such cases, process control is directed to an
operation 1820 where a fetal fraction value from an independent
source is considered. Such operation may be performed at the time
the bin under consideration is evaluated or at an earlier time. In
either case, the independent source typically does not employ the
amount of nucleic acid in the bin under consideration as a basis
for its calculation of fetal fraction. Examples of independent
sources of information for the fetal fraction calculation include
polymorphisms in nucleic acid sequences outside the bin under
consideration, X chromosome counts in samples for male fetuses,
etc. See U.S. patent application Ser. No. 12/958,347 filed Dec. 1,
2010; U.S. patent application Ser. No. 12/958,356 filed Dec. 1,
2010; and U.S. patent application Ser. No. 13/445,778 filed Apr.
12, 2012, each previously incorporated by reference.
Next, the two fetal fraction values are compared and it is
determined whether or not they agree with one another. See decision
operation 1822. If they agree, there is good evidence to conclude
that the bin under consideration encompasses a copy number
variation within at least one of the genomes present in the sample
under evaluation.
In certain embodiments, however, the bin under consideration is
further evaluated, with a finer resolution as indicated in
operation 1826. In such cases, the process may analyze subregions
within the current bin in order to identify copy number variations
within the sub-bins. For example, if the bin size is 1 Mb, the
sub-bins may each be 100 kb. To establish the presence of a copy
number variation, the process may confirm that there are copy
number variations in multiple sub-bins and that these copy number
variations are clustered around a particular location or locations
within the larger bin. See block 1828. For example, confirmation
might require that at least two sub-bins within a group of four
contiguous sub-bins possess copy number variations.
In certain embodiments, the sub-bins are analyzed for copy number
variations using z-scores or a related representation as described
above. In some implementations, the technique described in
operations 1807, 1809, 1810, and 1813 may be used. For example, the
process may require that at least two 100 kb bins within four
contiguous bins have a z-score greater than 4 in order validate a
copy number variation of the larger bin under consideration (e.g.,
a 1 Mb bin). In some implementations, the sub-bin analysis allows
one to localize the copy number variation for the bin under
consideration into one or more sub-regions under consideration.
Other techniques for confirming that the copy number variation in
the current bin is supported by the copy number variations
identified in the sub-bins may be employed. For example, fetal
fraction values may be calculated based on the sub-bin z-scores (as
described above for bins) and such fetal fraction values may be
compared with an independently calculated fetal fraction value.
Agreement can be checked on a per sub-bin basis. Sub-bins having
fetal fraction values agreeing with the independently calculated
fetal fraction value are deemed to possess a copy number
variation.
When the two values fetal fraction compared in operation 1822 do
not agree, the basis for the detected copy number variation is
questioned. In the depicted process flow, this situation is
addressed in operation 1824, which is further illustrated in FIG.
18C. In such cases, one may conclude that (1) the genome present in
greater concentration (e.g., the maternal genome) has a copy number
variation in the bin under consideration, (2) less than the entire
sequence of the bin under considerations harbors the copy number
variation, or (3) the fetus is a mosaic. When the copy number
variation is present only in the genome present in greater
concentration, the fetal fraction calculated using the copy number
variation may be very large (e.g., 70% greater of lesser than
calculated by an independent technique).
If operation 1824 determines that the sample contains nucleic acid
from a mosaic, the process is concluded at 1832. In other words, no
further bins are considered. However, if operation 1824 determines
that one or more of the sub-bins possess a copy number variation,
those sub-bins are noted and process control transfers to operation
1830, which determines whether any other bins remain to be
considered. Similarly, after operation 1828 is completed, the
evaluation of the nucleic acid in the bin under consideration is
concluded and operation 1830 determines whether any further bins
remain to be considered in the reference genome. If no further bins
need to be considered, the process is completed as illustrated at
1832. If on the other hand, further bins need be considered,
process control is directed back to operation 1805 where the next
bin the reference genome is considered.
Information can be gleaned from contiguous bins having established
copy number variations. For example, if all bins within a given
chromosome have the same copy number variation, then it can be
established that a full chromosomal aneuploidy exists (e.g., T21).
If a subset of the bins in a chromosome have copy number
variations, then a partial aneuploidy is established and localized.
Bin or sub-bin analysis may also permit one to identify the origin
of genomic material in an unbalanced translocation identified by
karyotyping. See for example, Example 20 and the associated
discussion of FIG. 71.
Turning now to FIG. 18C, one implementation of operation 1824 is
described. As explained, the operation is triggered when the fetal
fraction values compared in operation 1822 do not agree. This
suggests that the bin under consideration contains only a partial
copy number variation or the sample contains nucleic acid from a
mosaic. Operation 1824 can distinguish these situations, and may
also provide further insight into the location of copy number
variation, if one is actually present in the bin under
consideration.
As depicted in FIG. 18C, the process iterates over each of the
sub-bins in the current bin. See process operation 1852. As an
example, there may be 10 sub-bins in a 1 Mb bin, with each of the
sub-bins being 100 kb in length. Each sub-bin is separately
considered in process 1824.
Within each iteration, the process initially determines the
quantity of sequenced nucleic acid present in the current sub-bin.
This quantity may be obtained by counting the tags mapping to the
current sub-bin as determined from, for example, the alignment
operation performed in operation 1807 of FIG. 18B. Next, the
quantity of sequenced nucleic acid in the current sub-bin is
optionally normalized to account for biases in the process. This
operation may be performed in a similar manner to that employed in
operation 1809 of FIG. 18B. As an example, the quantity of nucleic
acid in the current sub-bin may be normalized using the quantities
of nucleic acid in 10 other sub-bins having GC contents similar to
that of the sub-bin under consideration.
Next, as represented by block 1858 of FIG. 18C, the normalized
quantity of nucleic acid in the current sub-bin is recast to a
statistically relevant form such as a z-score. In certain
embodiments, operation 1858 corresponds to operation 1810 of FIG.
18B, but is performed for sub-bin sized sequences. After operation
1858, a value of fetal fraction is calculated using the quantity of
sequenced nucleic acid present in the current sub-bin. See
operation 1860. This process may be performed in a similar manner
as operation 1815 of FIG. 18B. Typically, the fetal fraction is
calculated using an expression that assumes a complete duplication
or deletion of the sequence within the boundaries of the current
sub-bin. Equation 31 presented in Example 20 may be used for this
purpose. Of course, the z-score and coefficient of variation values
would need to be calculated using sub-bin sized sequences.
In operation 1862, fetal fraction from an independent source is
provided for comparison with fetal fraction calculated in operation
1860. As an example, the fetal fraction provided in operation 1862
may be the same as that provided in operation 1820 of FIG. 18B. A
comparison of these two values fetal fraction is made in operation
1864. An agreement in the fetal fraction values for a given sub-bin
within the bin under consideration confirms a copy number variation
in the bin under consideration. See block 1868. In other words, the
assumption implicit in the method of calculating fetal fraction in
operation 1860 is correct. That assumption may be that there is a
complete duplication or complete deletion in the noted sub-bins. At
this point, the process of FIG. 18C concludes as indicated at 1872.
In such case, process control is direct to operation 1830 in FIG.
18B. Optionally, additional sub-bins are analyzed in the current
bin to localize the copy number variation with high resolution.
This allows the process to localize, with high-resolution, where
the copy number variations exist in the bin under
consideration.
As illustrated in FIG. 18C, if the fetal fraction values do not
agree, as determined in operation 1864, the process determines
whether there are any further sub-bins to consider within the
current bin. See operation 1866. If additional sub-bins remain to
be considered, process control is directed back to 1852 where the
next sub-bin in the current bin is considered. If, on the other
hand, no further sub-bins remain to be considered (operation 1866
is answered in the negative), the process concludes that no fetal
fraction values considered in operation 1864 agree for any of the
sub-bins considered in the bin under consideration. In this case,
it is concluded that the sample under consideration includes
nucleic acid from a mosaic. See item 1870. In other words, the
fetus providing nucleic acid to the sample comprises two genomes,
one of which harbors an aneuploidy. Process control returns to item
1832 in FIG. 18B.
A mosaic possesses two genomes. If neither genome possesses a copy
number variation, then no surprising result should occur. In other
words, operation 1813 of FIG. 18B would be answered in the
negative. However, if one of the genomes in the mosaic contains a
copy number variation, then that will be observed in operation
1813. However, the assumption that goes into producing the fetal
fraction value for the bin under consideration will never be valid
in the case of a mosaic, because the fetal DNA from the sample will
include two genomes, one which is normal diploid and the other
which is aneuploid. The relative ratio of these two genomes in the
bin under consideration will influence the value of the fetal
fraction. Because there is always some contribution from the normal
diploid genome and some contribution from the aneuploidy genome,
the assumption implicit in the calculation of fetal fraction (i.e.,
that the bin or sub-bin under consideration contains a complete
deletion or complete duplication) will not be valid.
Calculating and Comparing True Fetal Fraction Using Polymorphisms
e.g. SNPs on the Affected Sample's Chromosome of Interest and on a
Chromosome Known not to be Aneuploid (e.g. Chromosome X) to
Determine the Presence or Absence of Complete or Partial
Aneuploidies in Male Fetuses
As explained, the fetal fraction (FF) that is determined using
informative polymorphic sequences e.g. informative SNPs, can be
used to distinguish complete chromosomal aneuploidies from partial
aneuploidies.
The presence or absence of an aneuploidy, whether partial or
complete, can be determined from the value of fetal fraction that
is determined using polymorphic target sequences present on a
chromosome of interest and compared to the value of the fetal
fraction determined using polymorphic target sequences present on a
different chromosome in the sample. In samples where the fetus is a
male, FF can be determined on a chromosome of interest, and
compared to FF that is determined for chromosome X in the same
sample. For example, given a maternal sample from a mother carrying
a male fetus with trisomy 21, polymorphic sequences e.g. sequences
comprising at least one informative SNP, are selected for being
present on chromosome 21 and on chromosome X; the polymorphic
target sequences are amplified, and sequenced, and the fetal
fraction is determined as described elsewhere herein.
Given that the fetal fraction is proportional to the amount of a
fetal chromosome in a sample, the fetal fraction determined using
polymorphic sequences present on a trisomic chromosome in a
maternal sample will be 1+1/2 times the fetal fraction determined
using polymorphic sequences on a chromosome known not to be
aneuploid e.g. chromosome X in a male fetus, in the same maternal
sample. For example, in a normal sample, when fetal fraction is
determined using a panel of polymorphisms on chromosome 21
(FF.sub.21), and fetal fraction is determined using a panel of
polymorphisms on chromosome X (FF.sub.X), which is known to be
unaffected in a male fetus, then FF.sub.21=FF.sub.X. However, if
the fetus is trisomic for chromosome 21, then, the fetal fraction
for a trisomic chromosome 21 (FF.sub.21) will equal one and a half
times the fetal fraction of chromosome X (FF.sub.X) in the same
sample (FF.sub.21=1.5*FF.sub.X). It follows that if
FF.sub.21<FF.sub.X, the analysis logic can conclude that there
is a partial deletion of chromosome 21 and/or the presence of
masaicism. If FF.sub.21>FF.sub.X, the analysis logic can
conclude that there is an increase in a portion of chromosome 21
e.g. a partial duplication or multiplication, or of a complete
duplication of chromosome 21 that was not accounted for in the
technique employed to calculate fetal fraction from chromosome 21.
The difference between the two outcomes can be resolved as a
partial duplication will result in a FF that is <1.5*FF.sub.X.
Alternatively, partial duplications, deletions or presence of
mosaicism can be determined by e.g. increasing the number of
polymorphic sequences on chromosome 21 to obtain multiple FF values
along the length of the chromosome, such that a localized presence
of a double or multiple value for the FF indicates an increase in a
portion of the chromosome. Alternatively, as would be the case for
a mosaic sample, the FF determined from the polymorphic sequences
remains unchanged throughout the length of the chromosome,
indicating an overall increase in the amount of the complete
chromosome, but which increase is less than that for FF.sub.X, as
described above. In cases where there is a loss of an entire
chromosome e.g. monosomy X, then the FF.sub.monosomy=1/2FF.sub.x.
Fetal fraction values obtained from informative polymorphic
sequences can be used in combination with sequence doses and their
normalized dose values e.g. NCV, NSV, to confirm the presence of a
complete aneuploidy.
Calculating Fetal Fraction from Chromosome Doses of Aneuploid
Sequences
NCVs for the chromosome of interest were calculated according to
the equation
.mu..sigma..times..times. ##EQU00026## where {circumflex over
(.mu.)}.sub.j and {circumflex over (.sigma.)}.sub.f are the
estimated mean and standard deviation, respectively, for the j-th
chromosome dose in a set of qualified samples, and x.sub.ij is the
observed j-th chromosome dose for test sample i.
In general, the chromosome dose for trisomies will increase in
proportion to the fetal fraction (ff). Therefore, the ff for a
chromosome dose in a sample containing a trisomic chromosome will
increase in proportion to the fetal fraction
.times..times..times. ##EQU00027## where R.sub.jA is the chromosome
dose (x.sub.ij) for chromosome j in an affected sample i, ff is the
expected fetal fraction in the unaffected (qualified) sample U, and
R.sub.jU is the chromosome dose in the unaffected sample. The
factor "2" is included based on the assumption that there is one
extra copy of the chromosome of interest. If other a different
assumption is made (e.g., there this a partial duplication of the
chromosome of interest, then the factor "2" does not represent
reality. Substituting the chromosome dose R.sub.A in equation
19
.sigma..times..times. ##EQU00028## where R.sub.jU is the equivalent
of {circumflex over (.mu.)}.sub.j, and .sigma..sub.jU is the
equivalent {circumflex over (.sigma.)}.sub.j; ff is solved as
follows:
.times..sigma..times..times..times..sigma..times..times..times..times..ti-
mes. ##EQU00029##
Therefore, the percent "ff.sub.(i)" can be determined for any
chromosome as: ff.sub.(i)=2*NCV.sub.jACV.sub.jU Equation 25
Using Fetal Fraction to Resolve No-Calls
The ability of determining significant differences in the
representation of one or more sequences present in a mixture of two
genomes is predicated on the relative contribution of sequences by
the first genome relative to the contribution of the second genome.
For example, noninvasive prenatal diagnosis using cfDNA in a
maternal sample is challenging because only a small portion of the
DNA sample is derived from the fetus. For prenatal diagnostic
assays, the background of maternal DNA provides a practical limit
on sensitivity, and therefore the fraction of fetal DNA present in
the maternal sample is an important parameter. The sensitivity of
fetal aneuploidy detection by counting DNA molecules depends on the
fetal DNA fraction and the number of molecules that are
counted.
Typically, about 1% of maternal test samples that analyzed for
fetal aneuploidies by massively parallel sequencing are "no-call"
samples for which insufficient sequencing information e.g. number
of fetal sequence tags, precludes a confident determination of the
presence or absence one or more fetal aneuploidies in the maternal
sample. The "no-call" determination can result from levels of fetal
cfDNA that are too low relative to the level of the maternal
contribution to the sample to provide sequencing information that
distinguishes the aneuploid sample from the sequencing information
determined in qualified samples. To determine whether the "no-call"
sample is or is not an aneuploid sample, fetal fraction determined
empirically, and/or derived from, e.g., NVC values and used to
confirm or deny the presence of chromosomal aneuploidies. As
described elsewhere herein, ff can be used to characterize the type
of aneuploidy present in a test sample. For example, for thresholds
setting the "no-call" zone between 2.5 and 4 NCV values, a test
sample having an NCV bordering the 4 NCV threshold and shown to
have a low (e.g. less than 3%) fetal fraction is likely to be an
affected sample. Conversely, a test sample having an NCV bordering
the 2.5 NCV threshold and shown to have a high (e.g. greater than
40%) fetal fraction is likely to be an unaffected sample. Resolving
the "no-call" samples can rely on one determination of fetal
fraction. Preferably, the fetal fraction is determined according to
two or more different methods, or from using NCVs determined from
two or more different chromosomes in the sample using the same
method. Similarly, fetal fraction can be used to assess whether
samples with NCVs marginally greater than 4 or marginally smaller
than NCVs of 2.5, may be false positive or false negative calls,
respectively.
Apparatus and Systems for Determining CNV
Analysis of the sequencing data and the diagnosis derived therefrom
are typically performed using various computer executed algorithms
and programs. Therefore, certain embodiments employ processes
involving data stored in or transferred through one or more
computer systems or other processing systems. Embodiments of the
invention also relate to apparatus for performing these operations.
This apparatus may be specially constructed for the required
purposes, or it may be a general-purpose computer (or a group of
computers) selectively activated or reconfigured by a computer
program and/or data structure stored in the computer. In some
embodiments, a group of processors performs some or all of the
recited analytical operations collaboratively (e.g., via a network
or cloud computing) and/or in parallel. A processor or group of
processors for performing the methods described herein may be of
various types including microcontrollers and microprocessors such
as programmable devices (e.g., CPLDs and FPGAs) and
non-programmable devices such as gate array ASICs or general
purpose microprocessors.
In addition, certain embodiments relate to tangible and/or
non-transitory computer readable media or computer program products
that include program instructions and/or data (including data
structures) for performing various computer-implemented operations.
Examples of computer-readable media include, but are not limited
to, semiconductor memory devices, magnetic media such as disk
drives, magnetic tape, optical media such as CDs, magneto-optical
media, and hardware devices that are specially configured to store
and perform program instructions, such as read-only memory devices
(ROM) and random access memory (RAM). The computer readable media
may be directly controlled by an end user or the media may be
indirectly controlled by the end user. Examples of directly
controlled media include the media located at a user facility
and/or media that are not shared with other entities. Examples of
indirectly controlled media include media that is indirectly
accessible to the user via an external network and/or via a service
providing shared resources such as the "cloud." Examples of program
instructions include both machine code, such as produced by a
compiler, and files containing higher level code that may be
executed by the computer using an interpreter.
In various embodiments, the data or information employed in the
disclosed methods and apparatus is provided in an electronic
format. Such data or information may include reads and tags derived
from a nucleic acid sample, counts or densities of such tags that
align with particular regions of a reference sequence (e.g., that
align to a chromosome or chromosome segment), reference sequences
(including reference sequences providing solely or primarily
polymorphisms), chromosome and segment doses, calls such as
aneuploidy calls, normalized chromosome and segment values, pairs
of chromosomes or segments and corresponding normalizing
chromosomes or segments, counseling recommendations, diagnoses, and
the like. As used herein, data or other information provided in
electronic format is available for storage on a machine and
transmission between machines. Conventionally, data in electronic
format is provided digitally and may be stored as bits and/or bytes
in various data structures, lists, databases, etc. The data may be
embodied electronically, optically, etc.
In one embodiment, the invention provides a computer program
product for generating an output indicating the presence or absence
of an aneuploidy e.g. a fetal aneuploidy or cancer, in a test
sample. The computer product may contain instructions for
performing any one or more of the above-described methods for
determining a chromosomal anomaly. As explained, the computer
product may include a non-transitory and/or tangible computer
readable medium having a computer executable or compilable logic
(e.g., instructions) recorded thereon for enabling a processor to
determine chromosome doses and, in some cases, whether a fetal
aneuploidy is present or absent. In one example, the computer
product comprises a computer readable medium having a computer
executable or compilable logic (e.g., instructions) recorded
thereon for enabling a processor to diagnose a fetal aneuploidy
comprising: a receiving procedure for receiving sequencing data
from at least a portion of nucleic acid molecules from a maternal
biological sample, wherein said sequencing data comprises a
calculated chromosome and/or segment dose; computer assisted logic
for analyzing a fetal aneuploidy from said received data; and an
output procedure for generating an output indicating the presence,
absence or kind of said fetal aneuploidy.
The sequence information from the sample under consideration may be
mapped to chromosome reference sequences to identify a number of
sequence tags for each of any one or more chromosomes of interest
and to identify a number of sequence tags for a normalizing segment
sequence for each of said any one or more chromosomes of interest.
In various embodiments, the reference sequences are stored in a
database such as a relational or object database, for example.
It should be understood that it is not practical, or even possible
in most cases, for an unaided human being to perform the
computational operations of the methods disclosed herein. For
example, mapping a single 30 bp read from a sample to any one of
the human chromosomes might require years of effort without the
assistance of a computational apparatus. Of course, the problem is
compounded because reliable aneuploidy calls generally require
mapping thousands (e.g., at least about 10,000) or even millions of
reads to one or more chromosomes.
The methods disclosed herein can be performed using a
computer-readable medium having stored thereon computer-readable
instructions for carrying out a method for identifying any CNV e.g.
chromosomal or partial aneuploidies. Thus, in one embodiment, the
invention provides a computer-readable medium having stored thereon
computer-readable instructions for carrying out a method for
identifying complete and partial chromosomal aneuploidies e.g.
fetal aneuploidies. Such instructions may include, for example,
instructions for (a) obtaining and/or storing in a computer
readable medium, at least temporarily, sequence information for
fetal and maternal nucleic acids in a sample; (b) using the stored
sequence information to computationally identify a number of
sequence tags from the mixture of fetal and maternal nucleic acids
for each of any one or more chromosomes of interest selected from
chromosomes 1-22, X and Y, and to identify a number of sequence
tags for at least one normalizing chromosome sequence for each of
the one or more chromosomes of interest; and (c) computationally
calculating, using the number of sequence tags identified for each
of the one or more chromosomes of interest and the number of
sequence tags identified for each normalizing chromosome sequence,
a single chromosome dose for each of the chromosomes of interest.
These instructions may be executed using one or more appropriately
designed or configured processors. The instructions may
additionally include comparing each of the chromosome doses to
associated threshold values, and thereby determining the presence
or absence of any four or more partial or complete different fetal
chromosomal aneuploidies in the sample. As explained above, there
are numerous variations on this process. All such variations can be
implemented in using processing and storage features as described
here.
In some embodiments, the instructions may further include
automatically recording information pertinent to the method such as
chromosome doses and the presence or absence of a fetal chromosomal
aneuploidy in a patient medical record for a human subject
providing the maternal test sample. The patient medical record may
be maintained by, for example, a laboratory, physician's office, a
hospital, a health maintenance organization, an insurance company,
or a personal medical record website. Further, based on the results
of the processor-implemented analysis, the method may further
involve prescribing, initiating, and/or altering treatment of a
human subject from whom the maternal test sample was taken. This
may involve performing one or more additional tests or analyses on
additional samples taken from the subject.
Disclosed methods can also be performed using a computer processing
system which is adapted or configured to perform a method for
identifying any CNV e.g. chromosomal or partial aneuploidies. Thus,
in one embodiment, the invention provides a computer processing
system which is adapted or configured to perform a method as
described herein. In one embodiment, the apparatus comprises a
sequencing device adapted or configured for sequencing at least a
portion of the nucleic acid molecules in a sample to obtain the
type of sequence information described elsewhere herein. The
apparatus may also include components for processing the sample.
Such components are described elsewhere herein.
Sequence or other data, can be input into a computer or stored on a
computer readable medium either directly or indirectly. In one
embodiment, a computer system is directly coupled to a sequencing
device that reads and/or analyzes sequences of nucleic acids from
samples. Sequences or other information from such tools are
provided via interface in the computer system. Alternatively, the
sequences processed by system are provided from a sequence storage
source such as a database or other repository. Once available to
the processing apparatus, a memory device or mass storage device
buffers or stores, at least temporarily, sequences of the nucleic
acids. In addition, the memory device may store tag counts for
various chromosomes or genomes, etc. The memory may also store
various routines and/or programs for analyzing the presenting the
sequence or mapped data. Such programs/routines may include
programs for performing statistical analyses, etc.
In one example, a user provides a sample into a sequencing
apparatus. Data is collected and/or analyzed by the sequencing
apparatus which is connected to a computer. Software on the
computer allows for data collection and/or analysis. Data can be
stored, displayed (via a monitor or other similar device), and/or
sent to another location. The computer may be connected to the
internet which is used to transmit data to a handheld device
utilized by a remote user (e.g., a physician, scientist or
analyst). It is understood that the data can be stored and/or
analyzed prior to transmittal. In some embodiments, raw data is
collected and sent to a remote user or apparatus that will analyze
and/or store the data. Transmittal can occur via the internet, but
can also occur via satellite or other connection. Alternately, data
can be stored on a computer-readable medium and the medium can be
shipped to an end user (e.g., via mail). The remote user can be in
the same or a different geographical location including, but not
limited to a building, city, state, country or continent.
In some embodiments, the methods also include collecting data
regarding a plurality of polynucleotide sequences (e.g., reads,
tags and/or reference chromosome sequences) and sending the data to
a computer or other computational system. For example, the computer
can be connected to laboratory equipment, e.g., a sample collection
apparatus, a nucleotide amplification apparatus, a nucleotide
sequencing apparatus, or a hybridization apparatus. The computer
can then collect applicable data gathered by the laboratory device.
The data can be stored on a computer at any step, e.g., while
collected in real time, prior to the sending, during or in
conjunction with the sending, or following the sending. The data
can be stored on a computer-readable medium that can be extracted
from the computer. The data collected or stored can be transmitted
from the computer to a remote location, e.g., via a local network
or a wide area network such as the internet. At the remote location
various operations can be performed on the transmitted data as
described below.
Among the types of electronically formatted data that may be
stored, transmitted, analyzed, and/or manipulated in systems,
apparatus, and methods disclosed herein are the following: Reads
obtained by sequencing nucleic acids in a test sample Tags obtained
by aligning reads to a reference genome or other reference sequence
or sequences The reference genome or sequence Sequence tag
density--Counts or numbers of tags for each of two or more regions
(typically chromosomes or chromosome segments) of a reference
genome or other reference sequences Identities of normalizing
chromosomes or chromosome segments for particular chromosomes or
chromosome segments of interest Doses for chromosomes or chromosome
segments (or other regions) obtained from chromosomes or segments
of interest and corresponding normalizing chromosomes or segments
Thresholds for calling chromosome doses as either affected,
non-affected, or no call The actual calls of chromosome doses
Diagnoses (clinical condition associated with the calls)
Recommendations for further tests derived from the calls and/or
diagnoses Treatment and/or monitoring plans derived from the calls
and/or diagnoses
These various types of data may be obtained, stored transmitted,
analyzed, and/or manipulated at one or more locations using
distinct apparatus. The processing options span a wide spectrum. At
one end of the spectrum, all or much of this information is stored
and used at the location where the test sample is processed, e.g.,
a doctor's office or other clinical setting. In other extreme, the
sample is obtained at one location, it is processed and optionally
sequenced at a different location, reads are aligned and calls are
made at one or more different locations, and diagnoses,
recommendations, and/or plans are prepared at still another
location (which may be a location where the sample was
obtained).
In various embodiments, the reads are generated with the sequencing
apparatus and then transmitted to a remote site where they are
processed to produce aneuploidy calls. At this remote location, as
an example, the reads are aligned to a reference sequence to
produce tags, which are counted and assigned to chromosomes or
segments of interest. Also at the remote location, the counts are
converted to doses using associated normalizing chromosomes or
segments. Still further, at the remote location, the doses are used
to generate aneuploidy calls.
Among the processing operations that may be employed at distinct
locations are the following: Sample collection Sample processing
preliminary to sequencing Sequencing Analyzing sequence data and
deriving aneuploidy calls Diagnosis Reporting a diagnosis and/or a
call to patient or health care provider Developing a plan for
further treatment, testing, and/or monitoring Executing the plan
Counseling
Any one or more of these operations may be automated as described
elsewhere herein. Typically, the sequencing and the analyzing of
sequence data and deriving aneuploidy calls will be performed
computationally. The other operations may be performed manually or
automatically.
Examples of locations where sample collection may be performed
include health practitioners' offices, clinics, patients' homes
(where a sample collection tool or kit is provided), and mobile
health care vehicles. Examples of locations where sample processing
prior to sequencing may be performed include health practitioners'
offices, clinics, patients' homes (where a sample processing
apparatus or kit is provided), mobile health care vehicles, and
facilities of aneuploidy analysis providers. Examples of locations
where sequencing may be performed include health practitioners'
offices, clinics, health practitioners' offices, clinics, patients'
homes (where a sample sequencing apparatus and/or kit is provided),
mobile health care vehicles, and facilities of aneuploidy analysis
providers. The location where the sequencing takes place may be
provided with a dedicated network connection for transmitting
sequence data (typically reads) in an electronic format. Such
connection may be wired or wireless and have and may be configured
to send the data to a site where the data can be processed and/or
aggregated prior to transmission to a processing site. Data
aggregators can be maintained by health organizations such as
Health Maintenance Organizations (HMOs).
The analyzing and/or deriving operations may be performed at any of
the foregoing locations or alternatively at a further remote site
dedicated to computation and/or the service of analyzing nucleic
acid sequence data. Such locations include for example, clusters
such as general purpose server farms, the facilities of an
aneuploidy analysis service business, and the like. In some
embodiments, the computational apparatus employed to perform the
analysis is leased or rented. The computational resources may be
part of an internet accessible collection of processors such as
processing resources colloquially known as the cloud. In some
cases, the computations are performed by a parallel or massively
parallel group of processors that are affiliated or unaffiliated
with one another. The processing may be accomplished using
distributed processing such as cluster computing, grid computing,
and the like. In such embodiments, a cluster or grid of
computational resources collective form a super virtual computer
composed of multiple processors or computers acting together to
perform the analysis and/or derivation described herein. These
technologies as well as more conventional supercomputers may be
employed to process sequence data as described herein. Each is a
form of parallel computing that relies on processors or computers.
In the case of grid computing these processors (often whole
computers) are connected by a network (private, public, or the
Internet) by a conventional network protocol such as Ethernet. By
contrast, a supercomputer has many processors connected by a local
high-speed computer bus.
In certain embodiments, the diagnosis (e.g., the fetus has Downs
syndrome or the patient has a particular type of cancer) is
generated at the same location as the analyzing operation. In other
embodiments, it is performed at a different location. In some
examples, reporting the diagnosis is performed at the location
where the sample was taken, although this need not be the case.
Examples of locations where the diagnosis can be generated or
reported and/or where developing a plan is performed include health
practitioners' offices, clinics, internet sites accessible by
computers, and handheld devices such as cell phones, tablets, smart
phones, etc. having a wired or wireless connection to a network.
Examples of locations where counseling is performed include health
practitioners' offices, clinics, internet sites accessible by
computers, handheld devices, etc.
In some embodiments, the sample collection, sample processing, and
sequencing operations are performed at a first location and the
analyzing and deriving operation is performed at a second location.
However, in some cases, the sample collection is collected at one
location (e.g., a health practitioner's office or clinic) and the
sample processing and sequencing is performed at a different
location that is optionally the same location where the analyzing
and deriving take place.
In various embodiments, a sequence of the above-listed operations
may be triggered by a user or entity initiating sample collection,
sample processing and/or sequencing. After one or more these
operations have begun execution the other operations may naturally
follow. For example, the sequencing operation may cause reads to be
automatically collected and sent to a processing apparatus which
then conducts, often automatically and possibly without further
user intervention, the sequence analysis and derivation of
aneuploidy operation. In some implementations, the result of this
processing operation is then automatically delivered, possibly with
reformatting as a diagnosis, to a system component or entity that
processes reports the information to a health professional and/or
patient. As explained such information can also be automatically
processed to produce a treatment, testing, and/or monitoring plan,
possibly along with counseling information. Thus, initiating an
early stage operation can trigger an end to end sequence in which
the health professional, patient or other concerned party is
provided with a diagnosis, a plan, counseling and/or other
information useful for acting on a physical condition. This is
accomplished even though parts of the overall system are physically
separated and possibly remote from the location of, e.g., the
sample and sequence apparatus.
FIG. 19 shows one implementation of a dispersed system for
producing a call or diagnosis from a test sample. A sample
collection location 01 is used for obtaining a test sample from a
patient such as a pregnant female or a putative cancer patient. The
samples then provided to a processing and sequencing location 03
where the test sample may be processed and sequenced as described
above. Location 03 includes apparatus for processing the sample as
well as apparatus for sequencing the processed sample. The result
of the sequencing, as described elsewhere herein, is a collection
of reads which are typically provided in an electronic format and
provided to a network such as the Internet, which is indicated by
reference number 05 in FIG. 19.
The sequence data is provided to a remote location 07 where
analysis and call generation are performed. This location may
include one or more powerful computational devices such as
computers or processors. After the computational resources at
location 07 have completed their analysis and generated a call from
the sequence information received, the call is relayed back to the
network 05. In some implementations, not only is a call generated
at location 07 but an associated diagnosis is also generated. The
call and or diagnosis are then transmitted across the network and
back to the sample collection location 01 as illustrated in FIG.
19. As explained, this is simply one of many variations on how the
various operations associated with generating a call or diagnosis
may be divided among various locations. One common variant involves
providing sample collection and processing and sequencing in a
single location. Another variation involves providing processing
and sequencing at the same location as analysis and call
generation.
FIG. 20 elaborates on the options for performing various operations
at distinct locations. In the most granular sense depicted in FIG.
20, each of the following operations is performed at a separate
location: sample collection, sample processing, sequencing, read
alignment, calling, diagnosis, and reporting and/or plan
development.
In one embodiment that aggregates some of these operations, sample
processing and sequencing are performed in one location and read
alignment, calling, and diagnosis are performed at a separate
location. See the portion of FIG. 20 identified by reference
character A. In another implementation, that is identified by
character B in FIG. 20, sample collection, sample processing, and
sequencing are all performed at the same location. In this
implementation, read alignment and calling are performed in a
second location. Finally, diagnosis and reporting and/or plan
development are performed in a third location. In the
implementation depicted by character C in FIG. 20, sample
collection is performed at a first location, sample processing,
sequencing, read alignment, calling, and diagnosis are all
performed together at a second location, and reporting and/or plan
development are performed at a third location. Finally, in the
implementation labeled D in FIG. 20, sample collection is performed
at a first location, sample processing, sequencing, read alignment,
and calling are all performed at a second location, and diagnosis
and reporting and/or plan management are performed at a third
location.
In one embodiment, the invention provides a system for use in
determining the presence or absence of any one or more different
complete fetal chromosomal aneuploidies in a maternal test sample
comprising fetal and maternal nucleic acids, the system including a
sequencer for receiving a nucleic acid sample and providing fetal
and maternal nucleic acid sequence information from the sample; a
processor; and a machine readable storage medium comprising
instructions for execution on said processor, the instructions
comprising:
(a) code for obtaining sequence information for said fetal and
maternal nucleic acids in the sample;
(b) code for using said sequence information to computationally
identify a number of sequence tags from the fetal and maternal
nucleic acids for each of any one or more chromosomes of interest
selected from chromosomes 1-22, X, and Y and to identify a number
of sequence tags for at least one normalizing chromosome sequence
or normalizing chromosome segment sequence for each of said any one
or more chromosomes of interest;
(c) code for using said number of sequence tags identified for each
of said any one or more chromosomes of interest and said number of
sequence tags identified for each normalizing chromosome sequence
or normalizing chromosome segment sequence to calculate a single
chromosome dose for each of the any one or more chromosomes of
interest; and
(d) code for comparing each of the single chromosome doses for each
of the any one or more chromosomes of interest to a corresponding
threshold value for each of the one or more chromosomes of
interest, and thereby determining the presence or absence of any
one or more complete different fetal chromosomal aneuploidies in
the sample.
In some embodiments, the code for calculating a single chromosome
dose for each of the any one or more chromosomes of interest
comprises code for calculating a chromosome dose for a selected one
of the chromosomes of interest as the ratio of the number of
sequence tags identified for the selected chromosome of interest
and the number of sequence tags identified for a corresponding at
least one normalizing chromosome sequence or normalizing chromosome
segment sequence for the selected chromosome of interest.
In some embodiments, the system further comprises code for
repeating the calculating of a chromosome dose for each of any
remaining chromosome segments of the any one or more segments of
any one or more chromosomes of interest.
In some embodiments, the one or more chromosomes of interest
selected from chromosomes 1-22, X, and Y comprise at least twenty
chromosomes selected from chromosomes 1-22, X, and Y, and wherein
the instructions comprise instructions for determining the presence
or absence of at least twenty different complete fetal chromosomal
aneuploidies is determined.
In some embodiments, the at least one normalizing chromosome
sequence is a group of chromosomes selected from chromosomes 1-22,
X, and Y. In other embodiments, the at least one normalizing
chromosome sequence is a single chromosome selected from
chromosomes 1-22, X, and Y.
In another embodiment, the invention provides a system for use in
determining the presence or absence of any one or more different
partial fetal chromosomal aneuploidies in a maternal test sample
comprising fetal and maternal nucleic acids, the system comprising:
a sequencer for receiving a nucleic acid sample and providing fetal
and maternal nucleic acid sequence information from the sample; a
processor; and a machine readable storage medium comprising
instructions for execution on said processor, the instructions
comprising:
(a) code for obtaining sequence information for said fetal and
maternal nucleic acids in said sample;
(b) code for using said sequence information to computationally
identify a number of sequence tags from the fetal and maternal
nucleic acids for each of any one or more segments of any one or
more chromosomes of interest selected from chromosomes 1-22, X, and
Y and to identify a number of sequence tags for at least one
normalizing segment sequence for each of said any one or more
segments of any one or more chromosomes of interest;
(c) code using said number of sequence tags identified for each of
said any one or more segments of any one or more chromosomes of
interest and said number of sequence tags identified for said
normalizing segment sequence to calculate a single chromosome
segment dose for each of said any one or more segments of any one
or more chromosomes of interest; and
(d) code for comparing each of said single chromosome segment doses
for each of said any one or more segments of any one or more
chromosomes of interest to a corresponding threshold value for each
of said any one or more chromosome segments of any one or more
chromosome of interest, and thereby determining the presence or
absence of one or more different partial fetal chromosomal
aneuploidies in said sample.
In some embodiments, the code for calculating a single chromosome
segment dose comprises code for calculating a chromosome segment
dose for a selected one of the chromosome segments as the ratio of
the number of sequence tags identified for the selected chromosome
segment and the number of sequence tags identified for a
corresponding normalizing segment sequence for the selected
chromosome segment.
In some embodiments, the system further comprises code for
repeating the calculating of a chromosome segment dose for each of
any remaining chromosome segments of the any one or more segments
of any one or more chromosomes of interest.
In some embodiments, the system further comprises (i) code for
repeating (a)-(d) for test samples from different maternal
subjects, and (ii) code for determining the presence or absence of
any one or more different partial fetal chromosomal aneuploidies in
each of said samples.
In other embodiments of any of the systems provided herein, the
code further comprises code for automatically recording the
presence or absence of a fetal chromosomal aneuploidy as determined
in (d) in a patient medical record for a human subject providing
the maternal test sample, wherein the recording is performed using
the processor.
In some embodiments of any of the systems provided herein, the
sequencer is configured to perform next generation sequencing
(NGS). In some embodiments, the sequencer is configured to perform
massively parallel sequencing using sequencing-by-synthesis with
reversible dye terminators. In other embodiments, the sequencer is
configured to perform sequencing-by-ligation. In yet other
embodiments, the sequencer is configured to perform single molecule
sequencing.
Kits.
In various embodiments, kits are provided for practice of the
methods described herein. In certain embodiments the kits comprise
one or more positive internal controls for a full aneuploidy and/or
for a partial aneuploidy. Typically, although not necessarily, the
controls comprise internal positive controls comprising nucleic
acid sequences of the type that are to be screened for. For
example, a control for a test to determine the presence or absence
of a fetal trisomy e.g. trisomy 21, in a maternal sample can
comprises DNA characterized by trisomy 21 (e.g., DNA obtained from
an individual with trisomy 21). In some embodiments, the control
comprises a mixture of DNA obtained from two or more individuals
with different aneuploidies. For example, for a test to determine
the presence or absence of trisomy 13, trisomy 18, trisomy 21, and
monosomy X, the control can comprise a combination of DNA samples
obtained from pregnant women each carrying a fetus with one of the
trisomys being tested. In addition to complete chromosomal
aneuploidies, IPCs can be created to provide positive controls for
tests to determine the presence or absence of partial
aneuploidies.
In certain embodiments the positive control(s) comprise one or more
nucleic acids comprising a trisomy 21 (T21), and/or a trisomy 18
(T18), and/or a trisomy 13 (T13). In certain embodiments the
nucleic acid(s) comprising each of the trisomys present are T21 are
provided in separate containers. In certain embodiments the nucleic
acids comprising two or more trisomys are provided in a single
container. Thus, for example, in certain embodiments, a container
may contain T21 and T18, T21 and T13, T18 and T13. In certain
embodiments, a container may contain T18, T21 and T13. In these
various embodiments, the trisomys may be provided in equal
quantity/concentration. In other embodiments, the trisomy may be
provided in particular predetermined ratios. In various embodiments
the controls can be provided as "stock" solutions of known
concentration.
In certain embodiments the control for detecting an aneuploidy
comprises a mixture of cellular genomic DNA obtained from a two
subjects one being the contributor of the aneuploid genome. For
example, as explained above, an internal positive control (IPC)
that is created as a control for a test to determine a fetal
trisomy e.g. trisomy 21, can comprise a combination of genomic DNA
from a male or female subject carrying the trisomic chromosome with
genomic DNA from a female subject known not to carry the trisomic
chromosome. In certain embodiments the genomic DNA is sheared to
provide fragments of between about 100-400 bp, between about
150-350 bp, or between about 200-300 bp to simulate the circulating
cfDNA fragments in maternal samples.
In certain embodiments the proportion of fragmented DNA from the
subject carrying the aneuploidy e.g. trisomy 21 in the control, is
chosen to simulate the proportion of circulating fetal cfDNA found
in maternal samples to provide an IPC comprising a mixture of
fragmented DNA comprising about 5%, about 10%, about 15%, about
20%, about 25%, about 30%, of DNA from the subject carrying the
aneuploidy. In certain embodiments the control comprise DNA from
different subjects each carrying a different aneuploidy. For
example, the IPC can comprise about 80% of the unaffected female
DNA, and the remaining 20% can be DNA from three different subjects
each carrying a trisomic chromosome 21, a trisomic chromosome 13,
and a trisomic chromosome 18.
In certain embodiments the control(s) comprise cfDNA obtained from
a mother known to carry a fetus with a known chromosomal
aneuploidy. For example, the controls can comprise cfDNA obtained
from a pregnant woman carrying a fetus with trisomy 21 and/or
trisomy 18, and/or trisomy 13. The cfDNA can extracted from the
maternal sample, and cloned into a bacterial vector and grown in
bacteria to provide an ongoing source of the IPC. Alternatively,
the cloned cfDNA can be amplified by e.g. PCR.
While the controls present in the kits are described above with
respect to trisomies, they need not be so limited. It will be
appreciated that the positive controls present in the kit can be
created to reflect other partial aneuploidies including for
example, various segment amplification and/or deletions. Thus, for
example, where various cancers are known to be associated with
particular amplifications or deletions of substantially complete
chromosomal arms the positive control(s) can comprise a p arm or a
q arm of any one or more of chromosomes 1-22, X and Y. In certain
embodiments the control comprises an amplification of one or more
arms selected from the group consisting of 1q, 3q, 4p, 4q, 5p, 5q,
6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p,
17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q, and/or 22q (see, e.g.,
Table 2).
In certain embodiments, the controls comprise aneuploidies for any
regions known to be associated with particular amplifications or
deletions (e.g., breast cancer associated with an amplification at
20Q13). Illustrative regions include, but are not limited to 17q23
(associated with breast cancer), 19q12 (associate with ovarian
cancer), 1q21-1q23 (associated with sarcomas and various solid
tumors), 8p11-p12 (associated with breast cancer), the ErbB2
amplicon, and so forth. In certain embodiments the controls
comprise an amplification or a deletion of a chromosomal region as
shown in any one of Tables 3-6. In certain embodiments the controls
comprise an amplification or a deletion of a chromosomal region
comprising a gene as shown in any one of Tables 3-6. In certain
embodiments the controls comprise nucleic acid sequences comprising
an amplification of a nucleic acid comprising one or more oncogenes
In certain embodiments the controls comprise nucleic acid sequences
comprising an amplification of a nucleic acid comprising one or
more genes selected from the group consisting of MYC, ERBB2 (EFGR),
CCND1 (Cyclin D1), FGFR1, FGFR2, HRAS, KRAS, MYB, MDM2, CCNE, KRAS,
MET, ERBB1, CDK4, MYCB, ERBB2, AKT2, MDM2 and CDK4.
The foregoing controls are intended to be illustrative and not
limiting. Using the teachings provided herein numerous other
controls suitable for incorporation into a kit will be recognized
by one of skill in the art.
In various embodiments in addition to the controls or instead of
the controls, the kits comprise one or more nucleic acids and/or
nucleic acid mimics that provide marker sequence(s) suitable for
tracking and determining sample integrity. In certain embodiments
the markers comprise an antigenomic sequence. In certain
embodiments the marker sequences range in length from about 30 bp
up to about 600 bp in length or about 100 bp to about 400 bp in
length. In certain embodiments the marker sequence(s) are at least
30 bp (or nt) in length. In certain embodiments the marker is
ligated to an adaptor and the length of the adaptor-ligated marker
molecule is between about 200 bp (or nt) and about 600 bp (or nt),
between about 250 bp (or nt) and 550 bp (or nt), between about 300
bp (or nt) and 500 bp (or nt), or between about 350 and 450. In
certain embodiments, the length of the adaptor-ligated marker
molecule is about 200 bp (or nt). In certain embodiments the length
of a marker molecule can be about 150 bp (or nt), about 160 bp (or
nt), 170 bp (or nt), about 180 bp (or nt), about 190 bp (or nt) or
about 200 bp (or nt). In certain embodiments the length of marker
ranges up to about 600 bp (or nt).
In certain embodiments the kit provides at least two, or at least
three, or at least four, or at least five, or at least six, or at
least seven, or at least eight, or at least nine, or at least ten,
or at least 11, or at least 12, or at least 13, or at least 14, or
at least 15, or at least 16, or at least 17 m, or at least 18, or
at least 19, or at least 20, or at least 25, or at least 30, or at
least 35, or at least 40, or at least 50 different sequences.
In various embodiments, the markers comprise one or more DNAs or
the markers comprise one or more DNA mimetics. Suitable mimetics
include, but are not limited to morpholino derivatives, peptide
nucleic acids (PNA), and phosphorothioate DNA. In various
embodiments the markers are incorporated into the controls. In
certain embodiments the markers are incorporated into adaptor(s)
and/or provided ligated to adaptors.
In certain embodiments the kit further includes one or more
sequencing adaptors. Such adaptors include, but are not limited to
indexed sequencing adaptors. In certain embodiments the adaptors
comprise a single-stranded arm that include an index sequence and
one or more PCR priming sites.
In certain embodiments the kit further comprises a sample
collection device for collection of a biological sample. In certain
embodiments the sample collection device comprises a device for
collecting blood and, optionally a receptacle for containing blood.
In certain embodiments the kit comprises a receptacle for
containing blood and the receptacle comprises an anticoagulant
and/or cell fixative, and/or one or more antigenomic marker
sequence(s).
In certain embodiments the kit further comprises DNA extraction
reagents (e.g., a separation matrix and/or an elution solution).
The kits can also include reagents for sequencing library
preparation. Such reagents include, but are not limited to a
solution for end-repairing DNA, and/or a solution for dA-tailing
DNA, and/or a solution for adaptor ligating DNA.
In addition, the kits optionally include labeling and/or
instructional materials providing directions (e.g., protocols) for
the use of the reagents and/or devices provided in the kit. For
example, the instructional materials can teach the use of the
reagents to prepare samples and/or to determine copy number
variation in a biological sample. In certain embodiments the
instructional materials teach the use of the materials to detect a
trisomy. In certain embodiments the instructional materials teach
the use of the materials to detect a cancer or a predisposition to
a cancer.
While the instructional materials in the various kits typically
comprise written or printed materials they are not limited to such.
Any medium capable of storing such instructions and communicating
them to an end user is contemplated herein. Such media include, but
are not limited to electronic storage media (e.g., magnetic discs,
tapes, cartridges, chips), optical media (e.g., CD ROM), and the
like. Such media may include addresses to internet sites that
provide such instructional materials.
The various method, apparatus, systems and uses are described in
further detail in the following Examples which are not in any way
intended to limit the scope of the invention as claimed. The
attached Figures are meant to be considered as integral parts of
the specification and description of the invention. The following
examples are offered to illustrate, but not to limit the claimed
invention.
EXPERIMENTAL
Example 1
Sample Processing and cfDNA Extraction
Peripheral blood samples were collected from pregnant women in
their first or second trimester of pregnancy and who were deemed at
risk for fetal aneuploidy. Informed consent was obtained from each
participant prior to the blood draw. Blood was collected before
amniocentesis or chorionic villus sampling. Karyotype analysis was
performed using the chorionic villus or amniocentesis samples to
confirm fetal karyotype.
Peripheral blood drawn from each subject was collected in ACD
tubes. One tube of blood sample (approximately 6-9 mL/tube) was
transferred into one 15-mL low speed centrifuge tube. Blood was
centrifuged at 2640 rpm, 4.degree. C. for 10 min using Beckman
Allegra 6 R centrifuge and rotor model GA 3.8.
For cell-free plasma extraction, the upper plasma layer was
transferred to a 15-ml high speed centrifuge tube and centrifuged
at 16000.times.g, 4.degree. C. for 10 min using Beckman Coulter
Avanti J-E centrifuge, and JA-14 rotor. The two centrifugation
steps were performed within 72 h after blood collection. Cell-free
plasma comprising cfDNA was stored at -80.degree. C. and thawed
only once before amplification of plasma cfDNA or for purification
of cfDNA.
Purified cell-free DNA (cfDNA) was extracted from cell-free plasma
using the QIAamp Blood DNA Mini kit (Qiagen) essentially according
to the manufacturer's instruction. One milliliter of buffer AL and
100 .mu.l of Protease solution were added to 1 ml of plasma. The
mixture was incubated for 15 minutes at 56.degree. C. One
milliliter of 100% ethanol was added to the plasma digest. The
resulting mixture was transferred to QIAamp mini columns that were
assembled with VacValves and VacConnectors provided in the QIAvac
24 Plus column assembly (Qiagen). Vacuum was applied to the
samples, and the cfDNA retained on the column filters was washed
under vacuum with 750 .mu.l of buffer AW1, followed by a second
wash with 750 .mu.l of buffer AW24. The column was centrifuged at
14,000 RPM for 5 minutes to remove any residual buffer from the
filter. The cfDNA was eluted with buffer AE by centrifugation at
14,000 RPM, and the concentration determined using Qubit.TM.
Quantitation Platform (Invitrogen).
Example 2
Preparation and Sequencing of Primary and Enriched Sequencing
Libraries
a. Preparation of Sequencing Libraries--Abbreviated Protocol
(ABB)
All sequencing libraries i.e. primary and enriched libraries, were
prepared from approximately 2 ng of purified cfDNA that was
extracted from maternal plasma. Library preparation was performed
using reagents of the NEBNext.TM. DNA Sample Prep DNA Reagent Set 1
(Part No. E6000L; New England Biolabs, Ipswich, Mass.), for
Illumina.RTM. as follows. Because cell-free plasma DNA is
fragmented in nature, no further fragmentation by nebulization or
sonication was done on the plasma DNA samples. The overhangs of
approximately 2 ng purified cfDNA fragments contained in 40 .mu.l
were converted into phosphorylated blunt ends according to the
NEBNext.RTM. End Repair Module by incubating in a 1.5 ml microfuge
tube the cfDNA with 5 .mu.l 10.times. phosphorylation buffer, 2
.mu.l deoxynucleotide solution mix (10 mM each dNTP), 1 .mu.l of a
1:5 dilution of DNA Polymerase I, 1 .mu.l T4 DNA Polymerase and 1
.mu.l T4 Polynucleotide Kinase provided in the NEBNext.TM. DNA
Sample Prep DNA Reagent Set 1 for 15 minutes at 20.degree. C. The
enzymes were then heat inactivated by incubating the reaction
mixture at 75.degree. C. for 5 minutes. The mixture was cooled to
4.degree. C., and dA tailing of the blunt-ended DNA was
accomplished using 10 .mu.l of the dA-tailing master mix containing
the Klenow fragment (3' to 5' exo minus) (NEBNext.TM. DNA Sample
Prep DNA Reagent Set 1), and incubating for 15 minutes at
37.degree. C. Subsequently, the Klenow fragment was heat
inactivated by incubating the reaction mixture at 75.degree. C. for
5 minutes. Following the inactivation of the Klenow fragment, 1
.mu.l of a 1:5 dilution of Illumina Genomic Adaptor Oligo Mix (Part
No. 1000521; Illumina Inc., Hayward, Calif.) was used to ligate the
Illumina adaptors (Non-Index Y-Adaptors) to the dA-tailed DNA using
4 .mu.l of the T4 DNA ligase provided in the NEBNext.TM. DNA Sample
Prep DNA Reagent Set 1, by incubating the reaction mixture for 15
minutes at 25.degree. C. The mixture was cooled to 4.degree. C.,
and the adaptor-ligated cfDNA was purified from unligated adaptors,
adaptor dimers, and other reagents using magnetic beads provided in
the Agencourt AMPure XP PCR purification system (Part No. A63881;
Beckman Coulter Genomics, Danvers, Mass.). Eighteen cycles of PCR
were performed to selectively enrich adaptor-ligated cfDNA (25
.mu.l) using Phusion.RTM. High-Fidelity Master Mix (25 .mu.l;
Finnzymes, Woburn, Mass.) and Illumina's PCR primers (0.5 .mu.M
each) complementary to the adaptors (Part No. 1000537 and 1000537).
The adaptor-ligated DNA was subjected to PCR (98.degree. C. for 30
seconds; 18 cycles of 98.degree. C. for 10 seconds, 65.degree. C.
for 30 seconds, and 72.degree. C. for 30; final extension at
72.degree. C. for 5 minutes, and hold at 4.degree. C.) using
Illumina Genomic PCR Primers (Part Nos. 100537 and 1000538) and the
Phusion HF PCR Master Mix provided in the NEBNext.TM. DNA Sample
Prep DNA Reagent Set 1, according to the manufacturer's
instructions. The amplified product was purified using the
Agencourt AMPure XP PCR purification system (Agencourt Bioscience
Corporation, Beverly, Mass.) according to the manufacturer's
instructions available at
www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf.
The purified amplified product was eluted in 40 .mu.l of Qiagen EB
Buffer, and the concentration and size distribution of the
amplified libraries was analyzed using the Agilent DNA 1000 Kit for
the 2100 Bioanalyzer (Agilent technologies Inc., Santa Clara,
Calif.).
b. Preparation of Sequencing Libraries--Full-Length Protocol
The full-length protocol described here is essentially the standard
protocol provided by Illumina, and only differs from the Illumina
protocol in the purification of the amplified library. The Illumina
protocol instructs that the amplified library be purified using gel
electrophoresis, while the protocol described herein uses magnetic
beads for the same purification step. Approximately 2 ng of
purified cfDNA extracted from maternal plasma was used to prepare a
primary sequencing library using NEBNext.TM. DNA Sample Prep DNA
Reagent Set 1 (Part No. E6000L; New England Biolabs, Ipswich,
Mass.) for Illumina.RTM. essentially according to the
manufacturer's instructions. All steps except for the final
purification of the adaptor-ligated products, which was performed
using Agencourt magnetic beads and reagents instead of the
purification column, were performed according to the protocol
accompanying the NEBNext.TM. Reagents for Sample Preparation for a
genomic DNA library that is sequenced using the Illumina.RTM. GAIL
The NEBNext.TM. protocol essentially follows that provided by
Illumina, which is available at
grcf.jhml.edu/hts/protocols/11257047_ChIP_Sample Prep.pdf.
The overhangs of approximately 2 ng purified cfDNA fragments
contained in 40 .mu.l were converted into phosphorylated blunt ends
according to the NEBNext.RTM. End Repair Module by incubating the
40 .mu.l cfDNA with 5 .mu.l 10.times. phosphorylation buffer, 2
.mu.l deoxynucleotide solution mix (10 mM each dNTP), 1 .mu.l of a
1:5 dilution of DNA Polymerase I, 1 .mu.l T4 DNA Polymerase and 1
.mu.l T4 Polynucleotide Kinase provided in the NEBNext.TM. DNA
Sample Prep DNA Reagent Set 1 in a 200 .mu.l microfuge tube in a
thermal cycler for 30 minutes at 20.degree. C. The sample was
cooled to 4.degree. C., and purified using a QIAQuick column
provided in the QIAQuick PCR Purification Kit (QIAGEN Inc.,
Valencia, Calif.) as follows. The 50 .mu.l reaction was transferred
to 1.5 ml microfuge tube, and 250 .mu.l of Qiagen Buffer PB were
added. The resulting 300 .mu.l were transferred to a QIAquick
column, which was centrifuged at 13,000 RPM for 1 minute in a
microfuge. The column was washed with 750 .mu.l Qiagen Buffer PE,
and re-centrifuged. Residual ethanol was removed by an additional
centrifugation for 5 minutes at 13,000 RPM. The DNA was eluted in
39 .mu.l Qiagen Buffer EB by centrifugation. dA tailing of 34 .mu.l
of the blunt-ended DNA was accomplished using 16 .mu.l of the
dA-tailing master mix containing the Klenow fragment (3' to 5' exo
minus) (NEBNext.TM. DNA Sample Prep DNA Reagent Set 1), and
incubating for 30 minutes at 37.degree. C. according to the
manufacturer's NEBNext.RTM. dA-Tailing Module. The sample was
cooled to 4.degree. C., and purified using a column provided in the
MinElute PCR Purification Kit (QIAGEN Inc., Valencia, Calif.) as
follows. The 50 .mu.l reaction was transferred to 1.5 ml microfuge
tube, and 250 .mu.l of Qiagen Buffer PB were added. The 300 .mu.l
were transferred to the MinElute column, which was centrifuged at
13,000 RPM for 1 minute in a microfuge. The column was washed with
750 .mu.l Qiagen Buffer PE, and re-centrifuged. Residual ethanol
was removed by an additional centrifugation for 5 minutes at 13,000
RPM. The DNA was eluted in 15 .mu.l Qiagen Buffer EB by
centrifugation. Ten microliters of the DNA eluate were incubated
with 1 .mu.l of a 1:5 dilution of the Illumina Genomic Adapter
Oligo Mix (Part No. 1000521), 15 .mu.l of 2.times. Quick Ligation
Reaction Buffer, and 4 .mu.l Quick T4 DNA Ligase, for 15 minutes at
25.degree. C. according to the NEBNext.RTM. Quick Ligation Module.
The sample was cooled to 4.degree. C., and purified using a
MinElute column as follows. One hundred and fifty microliters of
Qiagen Buffer PE were added to the 30 .mu.l reaction, and the
entire volume was transferred to a MinElute column were transferred
to a MinElute column, which was centrifuged at 13,000 RPM for 1
minute in a microfuge. The column was washed with 750 .mu.l Qiagen
Buffer PE, and re-centrifuged. Residual ethanol was removed by an
additional centrifugation for 5 minutes at 13,000 RPM. The DNA was
eluted in 28 .mu.l Qiagen Buffer EB by centrifugation. Twenty three
microliters of the adaptor-ligated DNA eluate were subjected to 18
cycles of PCR (98.degree. C. for 30 seconds; 18 cycles of
98.degree. C. for 10 seconds, 65.degree. C. for 30 seconds, and
72.degree. C. for 30; final extension at 72.degree. C. for 5
minutes, and hold at 4.degree. C.) using Illumina Genomic PCR
Primers (Part Nos. 100537 and 1000538) and the Phusion HF PCR
Master Mix provided in the NEBNext.TM. DNA Sample Prep DNA Reagent
Set 1, according to the manufacturer's instructions. The amplified
product was purified using the Agencourt AMPure XP PCR purification
system (Agencourt Bioscience Corporation, Beverly, Mass.) according
to the manufacturer's instructions available at
www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf.
The Agencourt AMPure XP PCR purification system removes
unincorporated dNTPs, primers, primer dimers, salts and other
contaminates, and recovers amplicons greater than 100 bp. The
purified amplified product was eluted from the Agencourt beads in
40 .mu.l of Qiagen EB Buffer and the size distribution of the
libraries was analyzed using the Agilent DNA 1000 Kit for the 2100
Bioanalyzer (Agilent technologies Inc., Santa Clara, Calif.).
c. Analysis of Sequencing Libraries Prepared According to the
Abbreviated (a) and the Full-Length (b) Protocols
The electropherograms generated by the Bioanalyzer are shown in
FIGS. 21A and 21B. FIG. 21A shows the electropherogram of library
DNA prepared from cfDNA purified from plasma sample M24228 using
the full-length protocol described in (a), and FIG. 21B shows the
electropherogram of library DNA prepared from cfDNA purified from
plasma sample M24228 using the full-length protocol described in
(b). In both figures, peaks 1 and 4 represent the 15 bp Lower
Marker, and the 1,500 Upper Marker, respectively; the numbers above
the peaks indicate the migration times for the library fragments;
and the horizontal lines indicate the set threshold for
integration. The electrophoregram in FIG. 21A shows a minor peak of
fragments of 187 bp and a major peak of fragments of 263 bp, while
the electropherogram in FIG. 21B shows only one peak at 265 bp.
Integration of the peak areas resulted in a calculated
concentration of 0.40 ng/.mu.l for the DNA of the 187 bp peak in
FIG. 21A, a concentration of 7.34 ng/.mu.l for the DNA of the 263
bp peak in FIG. 21A, and a concentration of 14.72 ng/.mu.l for the
DNA of the 265 bp peak in FIG. 21B. The Illumina adaptors that were
ligated to the cfDNA are known to be 92 bp, which when subtracted
from the 265 bp, indicate that the peak size of the cfDNA is 173
bp. It is possible that the minor peak at 187 bp represents
fragments of two primers that were ligated end-to-end. The linear
two-primer fragments are eliminated from the final library product
when the abbreviated protocol is used. The abbreviated protocol
also eliminates other smaller fragments of less than 187 bp. In
this example, the concentration of purified adaptor-ligated cfDNA
is double that of the adaptor-ligated cfDNA produced using the
full-length protocol. It has been noted that the concentration of
the adaptor-ligated cfDNA fragments was always greater than that
obtained using the full-length protocol (data not shown).
Thus, an advantage of preparing the sequencing library using the
abbreviated protocol is that the library obtained consistently
comprises only one major peak in the 262-267 bp range while the
quality of the library prepared using the full-length protocol
varies as reflected by the number and mobility of peaks other than
that representing the cfDNA. Non-cfDNA products would occupy space
on the flow cell and diminish the quality of the cluster
amplification and subsequent imaging of the sequencing reactions,
which underlies the overall assignment of the aneuploidy status.
The abbreviated protocol was shown not to affect the sequencing of
the library.
Another advantage of preparing the sequencing library using the
abbreviated protocol is that the three enzymatic steps of
blunt-ending, d-A tailing, and adaptor-ligation, take less than an
hour to complete to support the validation and implementation of a
rapid aneuploid diagnostic service.
Another advantage is that the three enzymatic steps of
blunt-ending, d-A tailing, and adaptor ligation, are performed in
the same reaction tube, thus avoiding multiple sample transfers
that would potentially lead to loss of material, and more
importantly to possible sample mix-up and sample contamination.
Example 3
Preparation of Sequencing Libraries from Unrepaired cfDNA: Adaptor
Ligation in Solution
To determine whether the abbreviated protocol could be further
shortened to further expedite sample analysis, sequencing libraries
were made from unrepaired cfDNA, and sequenced using the Illumina
Genome Analyzer II as previously described.
cfDNA was prepared from peripheral blood samples as described
herein. Blunt-ending and phosphorylation of the 5'-phosphate
mandated by the published protocol for the Illumina platform were
not performed to provide the unrepaired cfDNA sample.
Omitting DNA repair or DNA repair and phosphorylation was
determined not to affect the quality or the yield of the sequencing
library (data not shown).
2-Step in Solution Method for Non-Indexed Unrepaired DNA
In a first set of experiments, the unrepaired cfDNA was subjected
to simultaneous dA tailing and adaptor ligation by combining both
Klenow Exo- and T4-DNA ligase in the same reaction mixture as
follows: Thirty microliters of cfDNA at a concentration between
20-150 pg/.mu.l were dA-tailed (5 .mu.l of 10.times.NEB buffer#2, 2
.mu.l of 10 nM dNTP, 1 .mu.l of 10 nM ATP, and 1 .mu.l of 5000 U/ml
of Klenow Exo-), and ligated to Illumina Y-adapters (1 .mu.l of a
1:15 dilution of a 3 .mu.M stock) using 1 .mu.l of a 400,000 U/ml
T4-DNA ligase, in a reaction volume of 50 .mu.l. The non-indexed
Y-adapters were from Illumina. The combined reactions were
incubated at 25.degree. C. for 30 minutes. The enzymes were heat
inactivated at 75.degree. C. for 5 minutes, and the reaction
products were stored at 10.degree. C.
The adaptor-ligated product was purified using SPRI beads
(Agencourt AMPure XP PCR purification system, Beckman Coulter
Genomics), and subjected to 18 cycles of PCR. The PCR-amplified
library was subjected to purification using SPRI, and was sequenced
using Illumina's Genome Analyzer IIx or HiSeq to obtain single-end
reads of 36 bp according to the manufacturer's instructions. A
large number of 36 bp reads were obtained, covering approximately
10% of the genome. Upon completion of sequencing of the sample, the
Illumina "Sequencer Control Software/Real-time Analysis"
transferred base call files in binary format to a network attached
storage device for data analysis. Sequence data was analyzed by
means of software designed to run on a Linux server that converts
the binary format base calls into human readable text files using
illumines "BCLConverter", then calls the Open Source "Bowtie"
program to align sequences to the reference human genome that is
derived from the hg18 genome provided by National Center for
Biotechnology Information (NCBI36/hg18, available on the world wide
web at
http://genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=16626010-
5).
The software reads the sequence data generated from the above
procedure that uniquely aligned to the genome from Bowtie output
(bowtieout.txt files). Sequence alignments with up to 2 base
mis-matches were allowed and included in alignment counts only if
they aligned uniquely to the genome. Sequence alignments with
identical start and end coordinates (duplicates) were excluded.
Between about 5 and 25 million 36 bp tags with 2 or less mismatches
were mapped uniquely to the human genome. All mapped tags were
counted and included in the calculation of chromosome doses in both
test and qualifying samples. Regions extending from base 0 to base
2.times.10.sup.6, base 10.times.10.sup.6 to base 13.times.10.sup.6,
and base 23.times.10.sup.6 to the end of chromosome Y, were
specifically excluded from the analysis because tags derived from
either male or female fetuses map to these regions of the
Y-chromosome.
FIG. 22A shows the average (n=16) of the percent of the total
number of sequence tags that mapped to each human chromosome (%
ChrN) when the sequencing library was prepared according to the
abbreviated protocol (ABB; .diamond.) and when the sequencing
library was prepared according to the repair-free 2-STEP method
(INSOL; .quadrature.). These data show that preparing the
sequencing library using the repair-free 2-STEP method resulted in
a greater percent of tags mapped to chromosomes with lower GC
content and a smaller percent of tags that mapped to chromosomes
with greater GC content, when compared to the percent tags that
mapped to the corresponding chromosomes when using the abbreviated
method. FIG. 22B relates the percent sequence tags as a function of
the size of the chromosome, and shows that the repair-free method
decreases the bias of sequencing. The regression coefficient for
mapped tags obtained from sequencing libraries prepared according
to the abbreviated protocol (ABB; A), and the in solution
repair-free protocol (2-STEP; .quadrature.) were R.sup.2=0.9332,
and R.sup.2=0.9806, respectively.
TABLE-US-00008 TABLE 8 Percent GC content/chromosome Size GC (Mbps)
(%) Chr1 247 41.37 Chr2 243 39.44 Chr3 199 38.74 Chr4 191 38.60
Chr5 181 39.35 Chr6 171 39.94 Chr7 159 39.78 Chr8 146 40.30 Chr9
140 40.17 Chr10 135 40.43 Chr11 134 41.37 Chr12 132 40.59 Chr13 114
38.24 Chr14 106 40.85 Chr15 100 41.80 Chr16 89 44.64 Chr17 79 45.01
Chr18 76 39.66 Chr19 63 48.21 Chr20 62 42.05 Chr21 47 40.68 Chr22
50 47.64 ChrX 155 39.26 ChrY 58 37.74
Comparison of the abbreviated to repair-free 2-STEP method was also
viewed as a ratio of the percent tags mapped to individual
chromosomes when using the repair-free method to the percent tags
mapped to the individual chromosomes when using the abbreviated
method as a function of the percent GC content of each chromosome.
The percent GC content relative to chromosome size was calculated
based on published information of chromosome sequences and binning
of GC content (Constantini et al., Genome Res 16:536-541 [2006])
and provided in Table 8. The results are given in FIG. 22C, which
shows that there was a noticeable decrease in the ratio for
chromosomes having a high GC content, and an increase in the ratio
for chromosomes having a low GC content. These data clearly show
the normalizing effect that the repair-free method has for
overcoming GC bias.
These data show that the repair-free method corrects for some of
the GC bias that is known to be associated with sequencing of
amplified DNA.
To determine whether the repair-free method affected the proportion
of fetal versus maternal cfDNA that was sequenced, the percent
number of tags that mapped to chromosomes x and Y were determined.
FIGS. 23A and 23B show bar diagrams providing mean and standard
deviation of the percent of tags mapped to chromosomes X (FIG. 23A;
% ChrX) and Y (FIG. 23B; % ChrY) obtained from sequencing 10
samples of cfDNA purified from plasma of 10 pregnant women. FIG.
23A shows that a greater number of tags mapped to the X chromosome
when using the repair-free method relative to that obtained using
the abbreviated method. FIG. 23B shows that the percent tags that
mapped to the Y chromosome when using the repair-free method was
not different from that when using the abbreviated method.
These data show that the repair-free method does not introduce any
bias for or against sequencing fetal versus maternal DNA i.e. the
proportion of fetal sequences that were sequenced was not altered
when using the repair-free method.
Taken together, these data show that the repair-free method does
not adversely affect the quality of the sequencing library, nor the
information obtained from sequencing the library. Excluding the DNA
repair step required by published protocols lowers the cost of
reagents, and expedites the preparation of the sequencing
library.
2-Step in Solution Method for Indexed Unrepaired DNA
In a second set of experiments, the unrepaired cfDNA was subjected
to dA tailing, followed by heat-inactivation of the Klenow Exo-,
and adaptor ligation. Exclusion of the heat-inactivation of the
Klenow Exo- did not affect either the yield or the quality of the
sequencing library when non-indexed Illumina adaptors, (which carry
a 21-base single-stranded arm) were used for the ligation.
To determine whether the repair-free method could be applied to
multiplexed sequencing, home-made indexed Y adaptors comprising a 6
base index sequence, were used to generate the libraries by
including or excluding heat-inactivation of the Klenow. Unlike
non-indexed adapters, indexed-adapters comprise a 43-base single
stranded arm which includes the index sequence and the PCR priming
sites.
Twelve different indexed-adapters identical to Illumina TruSeq
adapters were made starting with oligonucleotides obtained from
Integrated DNA Technologies (Coralville, Iowa). Oligonucleotide
sequences were obtained from published Illumina TruSeq
Indexed-adapter sequences. Oligonucleotides were dissolved to
obtain a 300 .mu.M final concentration Annealing buffer (10 mM
Tris, 1 mM EDTA, 50 mM NaCl, pH 7.5). Equimolar mixtures of
oligonucleotides, typically 10 .mu.l each at 300 .mu.M, that
comprise the two arms of any given indexed-adapter were mixed and
allowed to anneal (95.degree. C. for 6 minutes, followed by a slow,
controlled cooling from 95.degree. C. to 10.degree. C.). The final
150 .mu.M adapter was diluted to 7.5 .mu.M in 10 mM Tris, 1 mM
EDTA, pH 8 and stored at -20.degree. C. until use.
The data showed that when indexed adaptors were used, the library
preparation by the 2_STEP method did not work if active Klenow Exo-
was present in the same reaction with ligase and indexed adapter.
However, if Klenow Exo- was first heat-inactivated at 75.degree. C.
for 5 minutes prior to adding the ligase plus the indexed-adapter,
the 2-STEP method worked well. It is likely that when indexed
adapters and active Klenow Exo- are present together, the
strand-displacement activity of the Klenow Exo- enzyme results in
digestion of the long single-stranded DNA arms of the
indexed-adaptors, eliminating the PCR primer sites.
Electropherograms of sequencing libraries made using the same cfDNA
and enzymes, without and with a heat-inactivation step after the
Klenow Exo- reaction showed that including a heat-inactivation of
the Klenow Exo- prior to adding ligase and the indexed-adapter in
the 2-STEP method made a library with the expected profile, with
the major peak at 290 bp (data not shown). Accordingly, as the
repair-free method is applicable to multiplexed sequencing, all
experiments using indexed-Y-adapters were amended to include the
heat-inactivation of the Klenow Exo-.
Example 4
Preparation of Sequencing Libraries from Unrepaired cfDNA: Adaptor
Ligation on a Solid Surface (SS)
1-Step Solid Surface Method for Non-Indexed DNA
To determine whether the repair-free library process could be
simplified further, the repair-free sequencing library preparation
method described in Example 3 was configured to be performed on a
solid surface. Sequencing of the prepared libraries was performed
as described in Example 3.
cfDNA was prepared from peripheral blood samples as described in
Example 1. Polypropylene tubes were coated with streptavidin,
washed and a first set of biotinylated indexed-adaptors were bound
to the streptavidin-coated tubes as follows. Tubes of an 8-well PCR
tube strip (USA Scientific, Ocala, Fla.) were coated with 0.5
nmoles of Streptavidin (Thermo Scientific, Rockford, Ill.) in 50 ul
of PBS by incubating the SA overnight at 4.degree. C. The tubes
were washed four times with 200 .mu.l each 1XTE. 7.5 pmoles, 3.75
pmoles, 1.8 pmoles and 0.9 pmoles of Biotinylated-Index1-adapters
each in 50 .mu.l TE were added in duplicate to the SA-coated tubes,
and incubated at room temperature for 25 minutes. The unbound
adaptors were removed and the tubes were washed four times with 200
.mu.l of TE. Biotinylated Index1 adaptors were made as described in
Example 3, using Biotinylated Universal Adapter Oligonucleotide
purchased from IDT.
1-Step SS Method Using cfDNA from Non-Pregnant Subjects
In a second strip of PCR tubes control samples (NTC: no template
control) or 30 .mu.l of approximately 120 pg/.mu.l, i.e. about 32
fmoles of purified cfDNA obtained from a non-pregnant woman were
incubated at 37.degree. C. for 15 minutes with 5 units Klenow Exo-
in NEB Buffer #2 with 20 nmoles dNTP and 10 nmoles ATP in 50 .mu.l
reaction volume. Subsequently, the Klenow enzyme was deactivated by
incubating the reaction mixture for 5 min at 75.degree. C. The
Klenow-DNA mixture was transferred to the corresponding tubes
containing the SA-bound biotinylated adaptors, and the cfDNA was
ligated to the immobilized adaptors by incubating the mixture with
400 units T4-DNA Ligase in 10 .mu.l of 1.times.T4-DNA Ligase buffer
at 25.degree. C. for 15 minutes. Subsequently, 7.5 pmoles of
non-biotinylated Index1-adapters were ligated to the solid-phase
bound cfDNA by incubating it with 200 units of T4-DNA Ligase in 10
.mu.l buffer at 25.degree. C. for 15 minutes. The reaction mixture
was removed, and the tubes were washed 5 times with 200 .mu.l of TE
buffer. The adaptor-ligated cfDNA was amplified by PCR using 50
.mu.l of Phusion PCR mix [New England Biolabs)] containing 1 .mu.M
each P5 and P7 primers (IDT) and cycled as follows: [30 s
@98.degree. C., (10s@98.degree. C., 10 s@50.degree. C., 10 s@
60.degree. C., 10 s@72.degree. C.).times.18 cycles, 5' @72.degree.
C., 10.degree. C. incubation]. The resulting library product was
subjected to a SPRI cleaning [Beckman Coulter Genomics], and the
quality of the library assessed from the profile obtained by
analysis using a High Sensitivity Bioanalyzer chip [Agilent
Technologies, Santa Clara, Calif.]]. The profiles showed that
solid-phase sequencing library preparation of unrepaired cfDNA
provides high-yield and high quality sequencing libraries (data not
shown).
1-Step SS Method Using cfDNA from Pregnant Subjects
The solid-surface (SS) method was tested using cfDNA samples
obtained from pregnant women.
The cfDNA was prepared from 8 peripheral blood samples obtained
from pregnant women as described in Example 1, and sequencing
libraries were prepared from the purified cfDNA as described above.
The libraries were sequenced, and sequence information
analyzed.
FIG. 24 shows the ratio of the number of non-excluded sites (NE
sites) on the reference genome (hg18) and the total number of tags
mapped to the non-excluded sites for each of 5 samples from which
cfDNA was prepared and used to construct a sequencing library
according to the abbreviated protocol (ABB) described in Example 2
(filled bars), the in solution repair-free protocol (2-STEP; empty
bars) described in Example 18, and the solid surface repair-free
protocol (1-STEP; gray bars) described in the present example.
The data shown in FIG. 24 shows that the representation of
PCR-amplified sequences prepared according to the three protocols
is comparable, indicating that the solid surface method does not
skew the variety of sequences that are represented in the
library.
FIG. 25A shows that the number of sequence tags uniquely mapped to
each of the chromosomes when obtained from sequencing the library
prepared according to the repair-free solid surface method is
comparable to that obtained when using the in solution repair-free
2-STEP method described above. The data show that both repair-free
methods decrease the GC bias of the sequencing data.
FIG. 25B shows the relationship between the number of tags mapped
to the size of the chromosome to which the tags were mapped. The
regression coefficient for mapped tags obtained from sequencing
libraries prepared according to the abbreviated protocol (ABB), the
in solution repair-free protocol (2-STEP), and the solid surface
repair-free protocol (1-STEP) were R.sup.2=0.9352, R.sup.2=0.9802,
and R.sup.2=0.9807, respectively.
FIG. 25C shows the ratio of percent mapped sequence tags per
chromosome obtained from sequencing libraries prepared according to
the repair-free 2-STEP protocol and the tags per chromosome
obtained sequencing libraries prepared according to the abbreviated
protocol (ABB) as a function of the percent GC content of each
chromosome (O), and the ratio of percent mapped sequence tags per
chromosome obtained from sequencing libraries prepared according to
the repair-free 1-STEP protocol and the tags per chromosome
obtained sequencing libraries prepared according to the abbreviated
protocol (ABB) as a function of the percent GC content of each
chromosome (.quadrature.). Taken together, the data in FIGS. 25B
and 25C show that the 1-STEP and 2-STEP methods both show similar
GC normalization effects because both omit the DNA repair step of
the library process.
To determine whether the repair-free method affected the proportion
of fetal versus maternal cfDNA that was sequenced, the percent
number of tags that mapped to chromosomes x and Y were determined.
FIGS. 26A and 26B shows a comparison of means and standard
deviations of the percent of tags mapped to chromosomes X (FIG.
26A) and Y (FIG. 26B) obtained from sequencing 5 samples of cfDNA
purified from plasma of 5 pregnant women from the ABB, 2-STEP and
1-STEP methods. FIG. 26A shows that a greater number of tags mapped
to the X chromosome when using the repair-free methods (2-STEP and
1-STEP) relative to that obtained using the abbreviated method
(filled bar). FIG. 26B shows that the percent tags that mapped to
the Y chromosome when using the repair-free 2-STEP and 1-STEP
methods was not different from that when using the abbreviated
method.
These data show that the repair-free solid surface 1-STEP method
does not introduce any bias for or against sequencing fetal versus
maternal DNA i.e. the proportion of fetal sequences that were
sequenced was not altered when using the repair-free solid surface
method.
Taken together the data demonstrate that generating sequencing
libraries on a solid surface is an easy and viable option for
sequencing sample preparation.
Example 5
High-Throughput Compatibility of the Repair-Free Solid Surface
1-Step Library Preparation Method
To determine whether the Repair-Free 1-STEP method for preparing
libraries for sequencing by NGS technology, could be applied to
high-throughput sample processing, 96 libraries of cfDNA from 96
peripheral blood samples were prepared in a 96 well PCR plate
coated with SA-bound indexed adaptors. Sequencing of the prepared
libraries was performed as described in Example 5.
Coating of a first PCR plate with SA, and ligation of biotinylated
indexed adaptors was performed as described in Example 4. Each
column of wells of the 96-well plate was coated with a biotinylated
adaptor comprising a unique index. Using a second 96-well PCR
plate, 37 different cfDNAs in 30 .mu.l was subjected to dA tailing
in the presence of 10 .mu.l each of Klenow Master Mix at 37.degree.
C. for 15 minutes followed by inactivation of the Klenow enzyme at
75.degree. C. for 5 minutes. Several cfDNAs were used in multiple
wells for a total of 94 wells with cfDNA; 2 wells were used as
no-template controls. The dA-tailed cfDNA mixture was transferred
to the first PCR plate and ligated to the bound biotinylated
adaptors in the presence of 10 .mu.l Quick Ligase Master Mix 1 at
25.degree. C. for 15 minutes using the PCT-225 Gradient Tetrad
Thermal Cycler (BioRad, Hercules, Calif.). 10 .mu.l of Ligation
Master Mix2 customized for each indexed-adapter was added and
ligated at 5.degree. C. for 15 minutes. Unbound DNA was removed,
and the bound DNA-biotinylated adaptor complexes washed five times
with TE buffer. 50 .mu.l of PCR master mix was added to each well,
and the adaptor-ligated DNA was amplified and subjected to a SPRI
cleaning as described in Example 4. The libraries were diluted and
analyzed using HiSens BA chips.
A correlation between the amount of purified cfDNA used to prepare
the sequencing libraries and the resulting amount of library
product was made for 61 clinical samples prepared using the ABB
method (FIG. 27A), and 35 research samples prepared using the
repair-free SS 1-STEP method (FIG. 27B). These data show that the
correlation is considerably greater for libraries prepared using
the Repair-Free SS 1-STEP method (R2=0.5826; FIG. 27A) when
compared to that obtained for libraries prepared using the
abbreviated method described in Example 2 (R2=0.1534; FIG. 27B).
Note that the cfDNA samples in this comparison are not the same,
because clinical samples are not available for R&D. However,
these results indicate that the repair-free SS 1-STEP method has
consistently greater correlation between cfDNA input and library
output than the ABB method. The correlation was subsequently
compared for the 3 methods i.e. ABB, repair-free 2-STEP, and
repair-free SS 1-STEP methods using serially diluted amounts of the
same purified cfDNA for all three methods. As is shown in FIG. 28,
the best correlation was obtained when libraries were prepared
according to the SS 1-STEP method (R.sup.2=0.9457; .DELTA.),
followed by the 2-STEP method (R.sup.2=0.7666; .quadrature.), and
the ABB method which had a significantly lower correlation
(R.sup.2=0.0386; .diamond.). These data show that repair-free
methods, whether in solution or on a solid surface, provide
consistent and predictable yields than either methods that
end-modify [DNA repair and phosphorylation] cfDNA, whether
including or excluding purification of the repaired DNA and of the
dA tailed product.
The time taken for preparing the libraries according to the
solid-surface method described in this example was several times
less than that taken when the sequencing libraries were prepared
according to the abbreviated method. For example, 10-14 samples can
be prepared manually in approximately 4 hours using the ABB method,
and 96 or 192 libraries can be prepared manually in 4 and 5 hours,
respectively, when using the SS 1-STEP method. In addition, the SS
1-STEP method can be easily automated to prepare libraries in
multiple of 96 for multiplexed sequencing using NGS technologies.
Thus, the SS method would be suitable for commercial automated
high-throughput analysis of samples.
Analysis of the DNA libraries showed that solid-phase sequencing
library preparation of unrepaired cfDNA provides high-yield and
high quality sequencing libraries that can be configured for
automated processes to further expedite sample analysis requiring
massively parallel sequencing using NGS technologies. The solid
surface method is applicable to repaired DNA.
Example 6
Multiplex Sequencing of Libraries Prepared According to the 1-Step
SS Method
The library samples prepared on a 96-well plate by the SS 1-STEP
method (Example 20) were sequenced in a multiplexed manner with six
different indexed samples per lane of the Illumina HySeq sequencer
flow cell. Sequencing of the prepared libraries was performed as
described in Example 2. The data shown in FIG. 29 compares the
efficiency of indexing as evaluated by multiplexed sequencing
between the 2-STEP (filled bars) and SS 1-STEP (open bars). These
data demonstrate that the efficiency of indexing is not compromised
by preparing libraries on a solid surface. FIGS. 30A and 30B show
the percent of the total number of sequence tags that mapped to
each human chromosome (% ChrN; FIG. 30A) when the sequencing
library was prepared according to the 1 step solid surface method;
and FIG. 30B (R2=0.9807) shows the percent sequence tags as a
function of the size of the chromosome. FIGS. 30A and 30B show that
the GC bias of the SS 1-STEP method is same as that of the 2-STEP
method, because both processes use the DNA repair-free sample
preparation enzymatics.
FIG. 31 shows the percent sequence tags that mapped to the
Y-chromosome relative to the tags that mapped to the X-chromosome,
obtained from sequencing 42 libraries that were prepared using the
SS 1-STEP method with indexed adapters, and that were sequenced in
a multiplexed manner using Illumina's sequencing by synthesis with
reversible terminator technology. The data clearly differentiate
samples obtained from pregnant women carrying male fetuses from
those carrying female fetuses.
Example 7
Sample Processing and DNA Extraction
Peripheral blood samples were collected from pregnant women in
their first or second trimester of pregnancy and who were deemed at
risk for fetal aneuploidy. Informed consent was obtained from each
participant prior to the blood draw. Blood was collected before
amniocentesis or chorionic villus sampling. Karyotype analysis was
performed using the chorionic villus or amniocentesis samples to
confirm fetal karyotype.
Peripheral blood drawn from each subject was collected in ACD
tubes. One tube of blood sample (approximately 6-9 mL/tube) was
transferred into one 15-mL low speed centrifuge tube. Blood was
centrifuged at 2640 rpm, 4.degree. C. for 10 min using Beckman
Allegra 6 R centrifuge and rotor model GA 3.8.
For cell-free plasma extraction, the upper plasma layer was
transferred to a 15-ml high speed centrifuge tube and centrifuged
at 16000.times.g, 4.degree. C. for 10 min using Beckman Coulter
Avanti J-E centrifuge, and JA-14 rotor. The two centrifugation
steps were performed within 72 h after blood collection. Cell-free
plasma was stored at -80.degree. C. and thawed only once before DNA
extraction.
Cell-free DNA was extracted from cell-free plasma by using QIAamp
DNA Blood Mini kit (Qiagen) according to the manufacturer's
instructions. Five milliliters of buffer AL and 500 .mu.l of Qiagen
Protease were added to 4.5 ml--5 ml of cell-free plasma. The volume
was adjusted to 10 ml with phosphate buffered saline (PBS), and the
mixture was incubated at 56.degree. C. for 12 minutes. Multiple
columns were used to separate the precipitated cfDNA from the
solution by centrifugation at 8,000 RPM in a Beckman
microcentrifuge. The columns were washed with AW1 and AW2 buffers,
and the cfDNA was eluted with 55 .mu.l of nuclease-free water.
Approximately 3.5-7 ng of cfDNA was extracted from the plasma
samples.
All sequencing libraries were prepared from approximately 2 ng of
purified cfDNA that was extracted from maternal plasma. Library
preparation was performed using reagents of the NEBNext.TM. DNA
Sample Prep DNA Reagent Set 1 (Part No. E6000L; New England
Biolabs, Ipswich, Mass.), for Illumina.RTM. as follows. Because
cell-free plasma DNA is fragmented in nature, no further
fragmentation by nebulization or sonication was done on the plasma
DNA samples. The overhangs of approximately 2 ng purified cfDNA
fragments contained in 40 .mu.l were converted into phosphorylated
blunt ends according to the NEBNext.RTM. End Repair Module by
incubating in a 1.5 ml microfuge tube the cfDNA with 5 .mu.l
10.times. phosphorylation buffer, 2 .mu.l deoxynucleotide solution
mix (10 mM each dNTP), 1 .mu.l of a 1:5 dilution of DNA Polymerase
I, 1 .mu.l T4 DNA Polymerase and 1 .mu.l T4 Polynucleotide Kinase
provided in the NEBNext.TM. DNA Sample Prep DNA Reagent Set 1 for
15 minutes at 20.degree. C. The enzymes were then heat inactivated
by incubating the reaction mixture at 75.degree. C. for 5 minutes.
The mixture was cooled to 4.degree. C., and dA tailing of the
blunt-ended DNA was accomplished using 10 .mu.l of the dA-tailing
master mix containing the Klenow fragment (3' to 5' exo minus)
(NEBNext.TM. DNA Sample Prep DNA Reagent Set 1), and incubating for
15 minutes at 37.degree. C. Subsequently, the Klenow fragment was
heat inactivated by incubating the reaction mixture at 75.degree.
C. for 5 minutes. Following the inactivation of the Klenow
fragment, 1 .mu.l of a 1:5 dilution of Illumina Genomic Adaptor
Oligo Mix (Part No. 1000521; Illumina Inc., Hayward, Calif.) was
used to ligate the Illumina adaptors (Non-Index Y-Adaptors) to the
dA-tailed DNA using 4 .mu.l of the T4 DNA ligase provided in the
NEBNext.TM. DNA Sample Prep DNA Reagent Set 1, by incubating the
reaction mixture for 15 minutes at 25.degree. C. The mixture was
cooled to 4.degree. C., and the adaptor-ligated cfDNA was purified
from unligated adaptors, adaptor dimers, and other reagents using
magnetic beads provided in the Agencourt AMPure XP PCR purification
system (Part No. A63881; Beckman Coulter Genomics, Danvers, Mass.).
Eighteen cycles of PCR were performed to selectively enrich
adaptor-ligated cfDNA using Phusion.RTM. High-Fidelity Master Mix
(Finnzymes, Woburn, Mass.) and Illumina's PCR primers complementary
to the adaptors (Part No. 1000537 and 1000537). The adaptor-ligated
DNA was subjected to PCR (98.degree. C. for 30 seconds; 18 cycles
of 98.degree. C. for 10 seconds, 65.degree. C. for 30 seconds, and
72.degree. C. for 30 seconds; final extension at 72.degree. C. for
5 minutes, and hold at 4.degree. C.) using Illumina Genomic PCR
Primers (Part Nos. 100537 and 1000538) and the Phusion HF PCR
Master Mix provided in the NEBNext.TM. DNA Sample Prep DNA Reagent
Set 1, according to the manufacturer's instructions. The amplified
product was purified using the Agencourt AMPure XP PCR purification
system (Agencourt Bioscience Corporation, Beverly, Mass.) according
to the manufacturer's instructions available at
www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf.
The purified amplified product was eluted in 40 .mu.l of Qiagen EB
Buffer, and the concentration and size distribution of the
amplified libraries was analyzed using the Agilent DNA 1000 Kit for
the 2100 Bioanalyzer (Agilent technologies Inc., Santa Clara,
Calif.).
The amplified DNA was sequenced using Illumina's Genome Analyzer II
to obtain single-end reads of 36 bp. Only about 30 bp of random
sequence information are needed to identify a sequence as belonging
to a specific human chromosome. Longer sequences can uniquely
identify more particular targets. In the present case, a large
number of 36 bp reads were obtained, covering approximately 10% of
the genome. Upon completion of sequencing of the sample, the
Illumina "Sequencer Control Software" transferred image and base
call files to a Unix server running the Illumina "Genome Analyzer
Pipeline" software version 1.51. The Illumina "Gerald" program was
run to align sequences to the reference human genome that is
derived from the hg18 genome provided by National Center for
Biotechnology Information (NCBI36/hg18, available on the world wide
web at
http://genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=16626-
0105). The sequence data generated from the above procedure that
uniquely aligned to the genome was read from Gerald output
(export.txt files) by a program (c2c.pl) running on a computer
running the Linnux operating system. Sequence alignments with base
mis-matches were allowed and included in alignment counts only if
they aligned uniquely to the genome. Sequence alignments with
identical start and end coordinates (duplicates) were excluded.
Between about 5 and 15 million 36 bp tags with 2 or less mismatches
were mapped uniquely to the human genome. All mapped tags were
counted and included in the calculation of chromosome doses in both
test and qualifying samples. Regions extending from base 0 to base
2.times.10.sup.6, base 10.times.10.sup.6 to base 13.times.10.sup.6,
and base 23.times.10.sup.6 to the end of chromosome Y, were
specifically excluded from the analysis because tags derived from
either male or female fetuses map to these regions of the
Y-chromosome.
It was noted that some variation in the total number of sequence
tags mapped to individual chromosomes across samples sequenced in
the same run (inter-chromosomal variation), but substantially
greater variation was noted to occur among different sequencing
runs (inter-sequencing run variation).
Example 8
Dose and Variance for Chromosomes 13, 18, 21, X, and Y
To examine the extent of inter-chromosomal and inter-sequencing
variation in the number of mapped sequence tags for all
chromosomes, plasma cfDNA obtained from peripheral blood of 48
volunteer pregnant subjects was extracted and sequenced as
described in Example 7, and analyzed as follows.
The total number of sequence tags that were mapped to each
chromosome (sequence tag density) was determined. Alternatively,
the number of mapped sequence tags may be normalized to the length
of the chromosome to generate a sequence tag density ratio. The
normalization to chromosome length is not a required step, and can
be performed solely to reduce the number of digits in a number to
simplify it for human interpretation. Chromosome lengths that can
be used to normalize the sequence tags counts can be the lengths
provided on the world wide web at
genome.ucsc.edu/goldenPath/stats.html#hg18.
The resulting sequence tag density for each chromosome was related
to the sequence tag density of each of the remaining chromosomes to
derive a qualified chromosome dose, which was calculated as the
ratio of the sequence tag density for the chromosome of interest
e.g. chromosome 21, and the sequence tag density of each of the
remaining chromosomes i.e. chromosomes 1-20, 22 and X. Table 9
provides an example of the calculated qualified chromosome dose for
chromosomes of interest 13, 18, 21, X, and Y, determined in one of
the qualified samples. Chromosomes doses were determined for all
chromosomes in all samples, and the average doses for chromosomes
of interest 13, 18, 21, X and Y in the qualified samples are
provided in Tables 10 and 11, and depicted in FIGS. 32-36. FIGS.
32-36 also depict the chromosome doses for the test samples. The
chromosome doses for each of the chromosomes of interest in the
qualified samples provides a measure of the variation in the total
number of mapped sequence tags for each chromosome of interest
relative to that of each of the remaining chromosomes. Thus,
qualified chromosome doses can identify the chromosome or a group
of chromosomes i.e. normalizing chromosome that has a variation
among samples that is closest to the variation of the chromosome of
interest, and that would serve as ideal sequences for normalizing
values for further statistical evaluation. FIGS. 37 and 38 depict
the calculated average chromosome doses determined in a population
of qualified samples for chromosomes 13, 18, and 21, and
chromosomes X and Y.
In some instances, the best normalizing chromosome may not have the
least variation, but may have a distribution of qualified doses
that best distinguishes a test sample or samples from the qualified
samples i.e. the best normalizing chromosome may not have the
lowest variation, but may have the greatest differentiability.
Thus, differentiability accounts for the variation in chromosome
dose and the distribution of the doses in the qualified
samples.
Tables 10 and 11 provide the coefficient of variation as the
measure of variability, and student t-test values as a measure of
differentiability for chromosomes 18, 21, X and Y, wherein the
smaller the T-test value, the greatest the differentiability. The
differentiability for chromosome 13 was determined as the ratio of
difference between the mean chromosome dose in the qualified
samples and the dose for chromosome 13 in the only T13 test sample,
and the standard deviation of mean of the qualified dose.
The qualified chromosome doses also serve as the basis for
determining threshold values when identifying aneuploidies in test
samples as described in the following.
TABLE-US-00009 TABLE 9 Qualified Chromosome Dose for Chromosomes
13, 18, 21, X and Y (n = 1; sample #11342, 46 XY) Chromo- some chr
21 chr 18 chr 13 chr X chrY chr1 0.149901 0.306798 0.341832
0.490969 0.003958 chr2 0.15413 0.315452 0.351475 0.504819 0.004069
chr3 0.193331 0.395685 0.44087 0.633214 0.005104 chr4 0.233056
0.476988 0.531457 0.763324 0.006153 chr5 0.219209 0.448649 0.499882
0.717973 0.005787 chr6 0.228548 0.467763 0.521179 0.748561 0.006034
chr7 0.245124 0.501688 0.558978 0.802851 0.006472 chr8 0.256279
0.524519 0.584416 0.839388 0.006766 chr9 0.309871 0.634203 0.706625
1.014915 0.008181 chr10 0.25122 0.514164 0.572879 0.822817 0.006633
chr11 0.257168 0.526338 0.586443 0.8423 0.00679 chr12 0.275192
0.563227 0.627544 0.901332 0.007265 chr13 0.438522 0.897509 1
1.436285 0.011578 chr14 0.405957 0.830858 0.925738 1.329624
0.010718 chr15 0.406855 0.832697 0.927786 1.332566 0.010742 chr16
0.376148 0.769849 0.857762 1.231991 0.009931 chr17 0.383027
0.783928 0.873448 1.254521 0.010112 chr18 0.488599 1 1.114194
1.600301 0.0129 chr19 0.535867 1.096742 1.221984 1.755118 0.014148
chr20 0.467308 0.956424 1.065642 1.530566 0.012338 chr21 1 2.046668
2.280386 3.275285 0.026401 chr22 0.756263 1.547819 1.724572
2.476977 0.019966 chrX 0.305317 0.624882 0.696241 1 0.008061 chrY
37.87675 77.52114 86.37362 124.0572 1
TABLE-US-00010 TABLE 10 Qualified Chromosome Dose, Variance and
Differentiability for chromosomes 21, 18 and 13 21 18 (n = 35) (n =
40) Avg Stdev CV T Test Avg Stdev CV T Test chr1 0.15335 0.001997
1.30 3.18E-10 0.31941 0.008384 2.62 0.001675 chr2 0.15267 0.001966
1.29 9.87E-07 0.31807 0.001756 0.55 4.39E-05 chr3 0.18936 0.004233
2.24 1.04E-05 0.39475 0.002406 0.61 3.39E-05 chr4 0.21998 0.010668
4.85 0.000501 0.45873 0.014292 3.12 0.001349 chr5 0.21383 0.005058
2.37 1.43E-05 0.44582 0.003288 0.74 3.09E-05 chr6 0.22435 0.005258
2.34 1.48E-05 0.46761 0.003481 0.74 2.32E-05 chr7 0.24348 0.002298
0.94 2.05E-07 0.50765 0.004669 0.92 9.07E-05 chr8 0.25269 0.003497
1.38 1.52E-06 0.52677 0.002046 0.39 4.89E-05 chr9 0.31276 0.003095
0.99 3.83E-09 0.65165 0.013851 2.13 0.000559 chr10 0.25618 0.003112
1.21 2.28E-10 0.53354 0.013431 2.52 0.002137 chr11 0.26075 0.00247
0.95 1.08E-09 0.54324 0.012859 2.37 0.000998 chr12 0.27563 0.002316
0.84 2.04E-07 0.57445 0.006495 1.13 0.000125 chr13 0.41828 0.016782
4.01 0.000123 0.87245 0.020942 2.40 0.000164 chr14 0.40671 0.002994
0.74 7.33E-08 0.84731 0.010864 1.28 0.000149 chr15 0.41861 0.007686
1.84 1.85E-10 0.87164 0.027373 3.14 0.003862 chr16 0.39977 0.018882
4.72 7.33E-06 0.83313 0.050781 6.10 0.075458 chr17 0.41394 0.02313
5.59 0.000248 0.86165 0.060048 6.97 0.088579 chr18 0.47236 0.016627
3.52 1.3E-07 chr19 0.59435 0.05064 8.52 0.01494 1.23932 0.12315
9.94 0.231139 chr20 0.49464 0.021839 4.42 2.16E-06 1.03023 0.058995
5.73 0.061101 chr21 2.03419 0.08841 4.35 2.81E-05 chr22 0.84824
0.070613 8.32 0.02209 1.76258 0.169864 9.64 0.181808 chrX 0.27846
0.015546 5.58 0.000213 0.58691 0.026637 4.54 0.064883
TABLE-US-00011 TABLE 11 Qualified Chromosome Dose, Variance and
Differentiability for chromosomes 13, X, and Y Avg Stdev CV Diff
Avg Stdev CV T Test 13 (n = 47) X (n = 19) chr1 0.36536 0.01775
4.86 1.904 0.56717 0.025988 4.58 0.001013 chr2 0.36400 0.009817
2.70 2.704 0.56753 0.014871 2.62 chr3 0.45168 0.007809 1.73 3.592
0.70524 0.011932 1.69 chr4 0.52541 0.005264 1.00 3.083 0.82491
0.010537 1.28 chr5 0.51010 0.007922 1.55 3.944 0.79690 0.012227
1.53 1.29E-11 chr6 0.53516 0.008575 1.60 3.758 0.83594 0.013719
1.64 2.79E-11 chr7 0.58081 0.017692 3.05 2.445 0.90507 0.026437
2.92 7.41E-07 chr8 0.60261 0.015434 2.56 2.917 0.93990 0.022506
2.39 2.11E-08 chr9 0.74559 0.032065 4.30 2.102 1.15822 0.047092
4.07 0.000228 chr10 0.61018 0.029139 4.78 2.060 0.94713 0.042866
4.53 0.000964 chr11 0.62133 0.028323 4.56 2.081 0.96544 0.041782
4.33 0.000419 chr12 0.65712 0.021853 3.33 2.380 1.02296 0.032276
3.16 3.95E-06 chr13 1.56771 0.014258 0.91 2.47E-15 chr14 0.96966
0.034017 3.51 2.233 1.50951 0.05009 3.32 8.24E-06 chr15 0.99673
0.053512 5.37 1.888 1.54618 0.077547 5.02 0.002925 chr16 0.95169
0.080007 8.41 1.613 1.46673 0.117073 7.98 0.114232 chr17 0.98547
0.091918 9.33 1.484 1.51571 0.132775 8.76 0.188271 chr18 1.13124
0.040032 3.54 2.312 1.74146 0.072447 4.16 0.001674 chr19 1.41624
0.174476 12.32 1.306 2.16586 0.252888 11.68 0.460752 chr20 1.17705
0.094807 8.05 1.695 1.81576 0.137494 7.57 0.08801 chr21 2.33660
0.131317 5.62 1.927 3.63243 0.235392 6.48 0.00675 chr22 2.01678
0.243883 12.09 1.364 3.08943 0.34981 11.32 0.409449 chrX 0.66679
0.028788 4.32 1.114 chr2-6 0.46751 0.006762 1.45 4.066 chr3-6
0.50332 0.005161 1.03 5.260 chr_tot 1.13209 0.038485 3.40 2.7E-05 Y
(n = 26) Chr 1-22, X 0.00734 0.002611 30.81 1.8E-12
Examples of diagnoses of T21, T13, T18 and a case of Turner
syndrome obtained using the normalizing chromosomes, chromosome
doses and differentiability for each of the chromosomes of interest
are described in Example 9.
Example 9
Diagnosis of Fetal Aneuploidy Using Normalizing Chromosomes
To apply the use of chromosome doses for assessing aneuploidy in a
biological test sample, maternal blood test samples were obtained
from pregnant volunteers and cfDNA was prepared, sequenced and
analyzed as described in Examples 1 and 2.
Trisomy 21
Table 12 provides the calculated dose for chromosome 21 in an
exemplary test sample (#11403). The calculated threshold for the
positive diagnosis of T21 aneuploidy was set at >2 standard
deviations from the mean of the qualified (normal) samples. A
diagnosis for T21 was given based on the chromosome dose in the
test sample being greater than the set threshold. Chromosomes 14
and 15 were used as normalizing chromosomes in separate
calculations to show that either a chromosome having the lowest
variability e.g. chromosome 14, or a chromosome having the greatest
differentiability e.g. chromosome 15, can be used to identify the
aneuploidy. Thirteen T21 samples were identified using the
calculated chromosome doses, and the aneuploidy samples were
confirmed to be T21 bp karyotype.
TABLE-US-00012 TABLE 12 Chromosome Dose for a T21 aneuploidy
(sample #11403, 47 XY + 21) Chromosome Sequence Tag Dose for Chr
Chromosome Density 21 Threshold Chr21 333,660 0.419672 0.412696
Chr14 795,050 Chr21 333,660 0.441038 0.433978 Chr15 756,533
Trisomy 18
Table 13 provides the calculated dose for chromosome 18 in a test
sample (#11390). The calculated threshold for the positive
diagnosis of T18 aneuploidy was set at 2 standard deviations from
the mean of the qualified (normal) samples. A diagnosis for T18 was
given based on the chromosome dose in the test sample being greater
than the set threshold. Chromosome 8 was used as the normalizing
chromosome. In this instance chromosome 8 had the lowest
variability and the greatest differentiability. Eight T18 samples
were identified using chromosome doses, and were confirmed to be
T18 bp karyotype.
These data show that a normalizing chromosome can have both the
lowest variability and the greatest differentiability.
TABLE-US-00013 TABLE 13 Chromosome Dose for a T18 aneuploidy
(sample #11390, 47 XY + 18) Chromosome Sequence Tag Dose for Chr
Chromosome Density 18 Threshold Chr18 602,506 0.585069 0.530867
Chr8 1,029,803
Trisomy 13
Table 14 provides the calculated dose for chromosome 13 in a test
sample (#51236). The calculated threshold for the positive
diagnosis of T13 aneuploidy was set at 2 standard deviations from
the mean of the qualified samples. A diagnosis for T13 was given
based on the chromosome dose in the test sample being greater than
the set threshold. The chromosome dose for chromosome 13 was
calculated using either chromosome 5 or the group of chromosomes 3,
4, 5, and 6 as the normalizing chromosome. One T13 sample was
identified.
TABLE-US-00014 TABLE 14 Chromosome Dose for a T13 aneuploidy
(sample #51236, 47 XY + 13) Chromosome Sequence Tag Dose for Chr
Chromosome Density 13 Threshold Chr13 692,242 0.541343 0.52594 Chr5
1,278,749 Chr13 692,242 0.530472 0.513647 Chr3-6 1,304,954
[average]
The sequence tag density for chromosomes 3-6 is the average tag
counts for chromosomes 3-6.
The data show that the combination of chromosomes 3, 4, 5 and 6
provide a variability that is lower than that of chromosome 5, and
the greatest differentiability than any of the other
chromosomes.
Thus, a group of chromosomes can be used as the normalizing
chromosome to determine chromosome doses and identify
aneuploidies.
Turner Syndrome (Monosomy X)
Table 15 provides the calculated dose for chromosomes X and Y in a
test sample (#51238). The calculated threshold for the positive
diagnosis of Turner Syndrome (monosomy X) was set for the X
chromosome at <-2 standard deviations from the mean, and for the
absence of the Y chromosome at <-2 standard deviations from the
mean for qualified (normal) samples.
TABLE-US-00015 TABLE 15 Chromosome Dose for a Turners (XO)
aneuploidy (sample #51238, 45 X) Chromosome Sequence Tag Dose for
Chr Chromosome Density X and Chr Y Threshold ChrX 873,631 0.786642
0.803832 Chr4 1,110,582 ChrY 1,321 Chr_Total 856,623.6 0.001542101
0.00211208 (1-22, X) (Average)
A sample having an X chromosome dose less than that of the set
threshold was identified as having less than one X chromosome. The
same sample was determined to have a Y chromosome dose that was
less than the set threshold, indicating that the sample did not
have a Y chromosome. Thus, the combination of chromosome doses for
X and Y were used to identify the Turner Syndrome (monosomy X)
samples.
Thus, the method provided enables for the determination of CNV of
chromosomes. In particular, the method enables for the
determination of over- and under-representation chromosomal
aneuploidies by massively parallel sequencing of maternal plasma
cfDNA and identification of normalizing chromosomes for the
statistical analysis of the sequencing data. The sensitivity and
reliability of the method allow for accurate first and second
trimester aneuploidy testing.
Example 10
Determination of Partial Aneuploidy
The use of sequence doses was applied for assessing partial
aneuploidy in a biological test sample of cfDNA that was prepared
from blood plasma, and sequenced as described in Example 7. The
sample was confirmed by karyotyping to have been derived from a
subject with a partial deletion of chromosome 11.
Analysis of the sequencing data for the partial aneuploidy (partial
deletion of chromosome 11 i.e. q21-q23) was performed as described
for the chromosomal aneuploidies in the previous examples. Mapping
of the sequence tags to chromosome 11 in a test sample revealed a
noticeable loss of tag counts between base pairs 81000082-103000103
in the q arm of the chromosome relative to the tag counts obtained
for corresponding sequence on chromosome 11 in the qualified
samples (data not shown). Sequence tags mapped to the sequence of
interest on chromosome 11 (810000082-103000103 bp) in each of the
qualified samples, and sequence tags mapped to all 20 megabase
segments in the entire genome in the qualified samples i.e.
qualified sequence tag densities, were used to determine qualified
sequence doses as ratios of tag densities in all qualified samples.
The average sequence dose, standard deviation, and coefficient of
variation were calculated for all 20 megabase segments in the
entire genome, and the 20-megabase sequence having the least
variability was the identified normalizing sequence on chromosome 5
(13000014-33000033 bp) (See Table 16), which was used to calculate
the dose for the sequence of interest in the test sample (see Table
17). Table 16 provides the sequence dose for the sequence of
interest on chromosome 11 (810000082-103000103 bp) in the test
sample that was calculated as the ratio of sequence tags mapped to
the sequence of interest and the sequence tags mapped to the
identified normalizing sequence. FIG. 40 shows the sequence doses
for the sequence of interest in the 7 qualified samples (O) and the
sequence dose for the corresponding sequence in the test sample
(.diamond.). The mean is shown by the solid line, and the
calculated threshold for the positive diagnosis of partial
aneuploidy that was set 5 standard deviations from the mean is
shown by the dashed line. A diagnosis for partial aneuploidy was
based on the sequence dose in the test sample being less than the
set threshold. The test sample was verified by karyotyping to have
deletion q21-q23 on chromosome 11.
Therefore, in addition to identifying chromosomal aneuploidies, the
method of the invention can be used to identify partial
aneuploidies.
TABLE-US-00016 TABLE 16 Qualified Normalizing Sequence, Dose and
Variance for Sequence Chr11: 81000082-103000103 (qualified samples
n = 7) Chr11: 81000082-103000103 Avg Stdev CV Chr5: 1.164702
0.004914 0.42 13000014-33000033
TABLE-US-00017 TABLE 17 Sequence Dose for Sequence of Interest
(81000082-103000103) on Chromosome 11 (test sample 11206) Sequence
Chromosome Chromosome Tag Segment Dose for Segment Density Chr 11
(q21-q23) Threshold Chr11: 81000082-103000103 27,052 1.0434313
1.1401347 Chr5: 13000014-33000033 25,926
Example 11
Demonstration of Detection of Aneuploidy
Sequencing data obtained for the samples described in Examples 2
and 3, and shown in FIGS. 32-36 were further analyzed to illustrate
the sensitivity of the method in successfully identifying
aneuploidies in maternal samples. Normalized chromosome doses for
chromosomes 21, 18, 13 X and Y were analyzed as a distribution
relative to the standard deviation of the mean (Y-axis) and shown
in FIGS. 41A-41E. The normalizing chromosome used is shown as the
denominator (X-axis).
FIG. 41A shows the distribution of chromosome doses relative to the
standard deviation from the mean for chromosome 21 dose in the
unaffected samples (O) and the trisomy 21 samples (T21; .DELTA.)
when using chromosome 14 as the normalizing chromosome for
chromosome 21. FIG. 41B shows the distribution of chromosome doses
relative to the standard deviation from the mean for chromosome 18
dose in the unaffected samples (.smallcircle.) and the trisomy 18
samples (T18; .DELTA.) when using chromosome 8 as the normalizing
chromosome for chromosome 18. FIG. 41C shows the distribution of
chromosome doses relative to the standard deviation from the mean
for chromosome 13 dose in the unaffected samples (.smallcircle.)
and the trisomy 13 samples (T13; .DELTA.), using the average
sequence tag density of the group of chromosomes 3, 4, 5, and 6 as
the normalizing chromosome to determine the chromosome dose for
chromosome 13. FIG. 41D shows the distribution of chromosome doses
relative to the standard deviation from the mean for chromosome X
dose in the unaffected female samples (.smallcircle.), the
unaffected male samples (.DELTA.), and the monosomy X samples (XO;
+) when using chromosome 4 as the normalizing chromosome for
chromosome X. FIG. 41E shows the distribution of chromosome doses
relative to the standard deviation from the mean for chromosome Y
dose in the unaffected male samples (o the unaffected female sample
s (.DELTA.), and the monosomy X samples (+), when using the average
sequence tag density of the group of chromosomes 1-22 and X as the
normalizing chromosome to determine the chromosome dose for
chromosome Y.
The data show that trisomy 21, trisomy 18, trisomy 13 were clearly
distinguishable from the unaffected (normal) samples. The monosomy
X samples were easily identifiable as having chromosome X dose that
were clearly lower than those of unaffected female samples (FIG.
41D), and as having chromosome Y doses that were clearly lower than
that of the unaffected male samples (FIG. 41E).
Therefore the method provided is sensitive and specific for
determining the presence or absence of chromosomal aneuploidies in
a maternal blood sample.
Example 12
Determination of Fetal Chromosomal Abnormalities Using Massively
Parallel DNA Sequencing of Cell Free Fetal DNA from Maternal Blood:
Test Set 1 Independent of Training Set 1
The study was conducted by qualified site clinical research
personnel at 13 US clinic locations between April 2009 and July
2010 under a human subject protocol approved by institutional
review boards (IRBs) at each institution. Informed written consent
was obtained from each subject prior to study participation. The
protocol was designed to provide blood samples and clinical data to
support development of noninvasive prenatal genetic diagnostic
methods. Pregnant women, age 18 years or older were eligible for
inclusion. For patients undergoing clinically indicated CVS or
amniocentesis blood was collected prior to performance of the
procedure, and results of fetal karyotype was also collected.
Peripheral blood samples (two tubes or .about.20 mL total) were
drawn from all subjects in acid citrate dextrose (ACD) tubes
(Becton Dickinson). All samples were de-identified and assigned an
anonymous patient ID number. Blood samples were shipped overnight
to the laboratory in temperature controlled shipping containers
provided for the study. Time elapsed between blood draw and sample
receipt was recorded as part of the sample accessioning.
Site research coordinators entered clinical data relevant to the
patient's current pregnancy and history into study case report
forms (CRFs) using the anonymous patient ID number. Cytogenetic
analysis of fetal karyotype from invasive prenatal procedure
samples was performed per local laboratories and the results were
also recorded in study CRFs. All data obtained on CRFs were entered
into a clinical database the laboratory. Cell free plasma was
obtained from individual blood tubes utilizing at two-step
centrifugation process within 24-48 hours of sample of
venipuncture. Plasma from a single blood tube was sufficient for
sequencing analysis. Cell-free DNA was extracted from cell-free
plasma by using QIAamp DNA Blood Mini kit (Qiagen) according to the
manufacturer's instructions. Since the cell free DNA fragments are
known to be approximately 170 base pairs (bp) in length (Fan et
al., Clin Chem 56:1279-1286 [2010]) no fragmentation of the DNA was
required prior to sequencing.
For the training set samples, cfDNA was sent to Prognosys
Biosciences, Inc. (La Jolla, Calif.) for sequencing library
preparation (cfDNA blunt ended and ligated to universal adapters)
and sequencing using standard manufacturer protocols with the
Illumina Genome Analyzer IIx instrumentation
(http://www.illumina.com/). Single-end reads of 36 base pairs were
obtained. Upon completion of the sequencing, all base call files
were collected and analyzed. For the test set samples, sequencing
libraries were prepared and sequencing carried out on Illumina
Genome Analyzer IIx instrument. Sequencing library preparation was
performed as follows. The full-length protocol described is
essentially the standard protocol provided by Illumina, and only
differs from the Illumina protocol in the purification of the
amplified library: the Illumina protocol instructs that the
amplified library be purified using gel electrophoresis, while the
protocol described herein uses magnetic beads for the same
purification step. Approximately 2 ng of purified cfDNA that had
been extracted from maternal plasma was used to prepare a primary
sequencing library using NEBNext.TM. DNA Sample Prep DNA Reagent
Set 1 (Part No. E6000L; New England Biolabs, Ipswich, Mass.) for
Illumina.RTM. essentially according to the manufacturer's
instructions. All steps except for the final purification of the
adaptor-ligated products, which was performed using Agencourt
magnetic beads and reagents instead of the purification column,
were performed according to the protocol accompanying the
NEBNext.TM. Reagents for Sample Preparation for a genomic DNA
library that is sequenced using the Illumina.RTM. GAIL The
NEBNext.TM. protocol essentially follows that provided by Illumina,
which is available at
grcf.jhml.edu/hts/protocols/11257047_ChIP_Sample_Prep.pdf.
The overhangs of approximately 2 ng purified cfDNA fragments
contained in 40 .mu.l were converted into phosphorylated blunt ends
according to the NEBNext.RTM. End Repair Module by incubating the
40 .mu.l cfDNA with 5 .mu.l 10.times. phosphorylation buffer, 2
.mu.l deoxynucleotide solution mix (10 mM each dNTP), 1 .mu.l of a
1:5 dilution of DNA Polymerase I, 1 .mu.l T4 DNA Polymerase and 1
.mu.l T4 Polynucleotide Kinase provided in the NEBNext.TM. DNA
Sample Prep DNA Reagent Set 1 in a 200 .mu.l microfuge tube in a
thermal cycler for 30 minutes at 20.degree. C. The sample was
cooled to 4.degree. C., and purified using a QIAQuick column
provided in the QIAQuick PCR Purification Kit (QIAGEN Inc.,
Valencia, Calif.) as follows. The 50 .mu.l reaction was transferred
to 1.5 ml microfuge tube, and 250 .mu.l of Qiagen Buffer PB were
added. The resulting 300 .mu.l were transferred to a QIAquick
column, which was centrifuged at 13,000 RPM for 1 minute in a
microfuge. The column was washed with 750 .mu.l Qiagen Buffer PE,
and re-centrifuged. Residual ethanol was removed by an additional
centrifugation for 5 minutes at 13,000 RPM. The DNA was eluted in
39 .mu.l Qiagen Buffer EB by centrifugation. dA tailing of 34 .mu.l
of the blunt-ended DNA was accomplished using 16 .mu.l of the
dA-tailing master mix containing the Klenow fragment (3' to 5' exo
minus) (NEBNext.TM. DNA Sample Prep DNA Reagent Set 1), and
incubating for 30 minutes at 37.degree. C. according to the
manufacturer's NEBNext.RTM. dA-Tailing Module. The sample was
cooled to 4.degree. C., and purified using a column provided in the
MinElute PCR Purification Kit (QIAGEN Inc., Valencia, Calif.) as
follows. The 50 .mu.l reaction was transferred to 1.5 ml microfuge
tube, and 250 .mu.l of Qiagen Buffer PB were added. The 300 .mu.l
were transferred to the MinElute column, which was centrifuged at
13,000 RPM for 1 minute in a microfuge. The column was washed with
750 .mu.l Qiagen Buffer PE, and re-centrifuged. Residual ethanol
was removed by an additional centrifugation for 5 minutes at 13,000
RPM. The DNA was eluted in 15 .mu.l Qiagen Buffer EB by
centrifugation. Ten microliters of the DNA eluate were incubated
with 1 .mu.l of a 1:5 dilution of the Illumina Genomic Adapter
Oligo Mix (Part No. 1000521), 15 .mu.l of 2.times. Quick Ligation
Reaction Buffer, and 4 .mu.l Quick T4 DNA Ligase, for 15 minutes at
25.degree. C. according to the NEBNext.RTM. Quick Ligation Module.
The sample was cooled to 4.degree. C., and purified using a
MinElute column as follows. One hundred and fifty microliters of
Qiagen Buffer PE were added to the 30 .mu.l reaction, and the
entire volume was transferred to a MinElute column were transferred
to a MinElute column, which was centrifuged at 13,000 RPM for 1
minute in a microfuge. The column was washed with 750 .mu.l Qiagen
Buffer PE, and re-centrifuged. Residual ethanol was removed by an
additional centrifugation for 5 minutes at 13,000 RPM. The DNA was
eluted in 28 .mu.l Qiagen Buffer EB by centrifugation. Twenty three
microliters of the adaptor-ligated DNA eluate were subjected to 18
cycles of PCR (98.degree. C. for 30 seconds; 18 cycles of
98.degree. C. for 10 seconds, 65.degree. C. for 30 seconds, and
72.degree. C. for 30; final extension at 72.degree. C. for 5
minutes, and hold at 4.degree. C.) using Illumina Genomic PCR
Primers (Part Nos. 100537 and 1000538) and the Phusion HF PCR
Master Mix provided in the NEBNext.TM. DNA Sample Prep DNA Reagent
Set 1, according to the manufacturer's instructions. The amplified
product was purified using the Agencourt AMPure XP PCR purification
system (Agencourt Bioscience Corporation, Beverly, Mass.) according
to the manufacturer's instructions available at
www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf.
The Agencourt AMPure XP PCR purification system removes
unincorporated dNTPs, primers, primer dimers, salts and other
contaminates, and recovers amplicons greater than 100 bp. The
purified amplified product was eluted from the Agencourt beads in
40 .mu.l of Qiagen EB Buffer and the size distribution of the
libraries was analyzed using the Agilent DNA 1000 Kit for the 2100
Bioanalyzer (Agilent technologies Inc., Santa Clara, Calif.). For
both the training and test sample sets, single-end reads of 36 base
pairs were sequenced.
Data Analysis and Sample Classification
Sequence reads 36 bases in length were aligned to the human genome
assembly hg18 obtained from the UCSC database
(http://hgdownload.cse.ucsc.edu/goldenPath/hg18/bigZips/).
Alignments were carried out utilizing the Bowtie short read aligner
(version 0.12.5) allowing for up to two base mismatches during
alignment (Langmead et al., Genome Biol 10:R25 [2009]. Only reads
that unambiguously mapped to a single genomic location were
included. Genomic sites where reads mapped were counted and
included in the calculation of chromosome doses (see below).
Regions on the Y chromosome where sequence tags from male and
female fetuses map without any discrimination were excluded from
the analysis (specifically, from base 0 to base 2.times.10.sup.6;
base 10.times.10.sup.6 to base 13.times.10.sup.6; and base
23.times.10.sup.6 to the end of chromosome Y).
Intra-run and inter-run sequencing variation in the chromosomal
distribution of sequence reads can obscure the effects of fetal
aneuploidy on the distribution of mapped sequence sites. To correct
for such variation, a chromosome dose was calculated as the count
of mapped sites for a given chromosome of interest is normalized to
counts observed on a predetermined normalizing chromosome sequence.
As described previously, a normalized chromosome sequence can be
composed of a single chromosome or a group of chromosomes. The
normalizing chromosome sequence was first identified in a subset of
samples in the training set of samples that were unaffected i.e.
qualified samples having diploid karyotypes for chromosomes of
interest 21, 18, 13 and X, considering each autosome as a potential
denominator in a ratio of counts with our chromosomes of interest.
Denominator chromosomes i.e. normalizing chromosome sequences were
selected that minimized the variation of the chromosome doses
within and between sequencing runs. Each chromosome of interest was
determined to have a distinct normalizing chromosome sequence
(denominator) (Table 18). No single chromosome could be identified
as a normalizing chromosome sequence for chromosome 13 as no one
chromosome was determined to reduce the variability in the dose of
chromosome 13 across samples i.e. the spread of the NCV values for
chromosome 13 was not reduced sufficiently to allow for a correct
identification of a T13 aneuploidy. Chromosomes 2-6 were chosen
randomly and tested for their ability as a group to mimic the
behavior of chromosome 13. The group of chromosomes 2-6 was found
to diminish substantially the variability in the dose for
chromosome 13 in the training samples, and was thus chosen as the
normalizing chromosome sequence for chromosome 13. As described
above, the variability in chromosome dose for chromosome Y is
greater than 30 independently of which single chromosome is used as
the normalizing chromosome sequence in determining the chromosome Y
dose. The group of chromosomes 2-6 was found to diminish
substantially the variability in the dose for chromosome Y in the
training samples, and was thus chosen as the normalizing chromosome
sequence for chromosome Y.
The chromosome doses for each of the chromosomes of interest in the
qualified samples provides a measure of the variation in the total
number of mapped sequence tags for each chromosome of interest
relative to that of each of the remaining chromosomes. Thus,
qualified chromosome doses can identify the chromosome or a group
of chromosomes i.e. normalizing chromosome sequence that has a
variation among samples that is closest to the variation of the
chromosome of interest, and that would serve as ideal sequences for
normalizing values for further statistical evaluation.
Chromosome doses for all samples in the training set i.e. qualified
and affected, also serve as the basis for determining threshold
values when identifying aneuploidies in test samples as described
in the following.
TABLE-US-00018 TABLE 18 Normalizing Chromosome Sequences for
Determining Chromosome Doses Chromosome of Interest - Normalizing
Chromosome Chromosome of Numerator (Chr mapped Sequence -
Denominator Interest counts) (Chr mapped counts) 21 Chr 21 Chr 9 18
Chr 18 Chr 8 13 Chr 13 Sum(Chr 2-6) X Chr X Chr 6 Y Chr Y Sum(Chr
2-6)
For each chromosome of interest in each sample in the test set, a
normalizing value was determined and used to determine the presence
or absence of an aneuploidy. The normalizing value was calculated
as a chromosome dose that can be further computed to provide a
normalized chromosome value (NCV).
Chromosome Doses
For the test set, a chromosome dose was calculated for each
chromosome of interest, 21, 18, 13, X and Y for every sample. As
provided in Table 18 above, the chromosome dose for chromosome 21
was calculated as a ratio of the number of tags in the test sample
that mapped to chromosome 21 in the test sample, and the number of
tags in the test sample that mapped to chromosome 9; the chromosome
dose for chromosome 18 was calculated as a ratio of the number of
tags in the test sample that mapped to chromosome 18 in the test
sample, and the number of tags in the test sample that mapped to
chromosome 8; the chromosome dose for chromosome 13 was calculated
as a ratio of the number of tags in the test sample that mapped to
chromosome 13 in the test sample, and the number of tags in the
test sample that mapped to chromosomes 2-6; the chromosome dose for
chromosome X was calculated as a ratio of the number of tags in the
test sample that mapped to chromosome X in the test sample, and the
number of tags in the test sample that mapped to chromosome 6; and
the chromosome dose for chromosome Y was calculated as a ratio of
the number of tags in the test sample that mapped to chromosome Y
in the test sample, and the number of tags in the test sample that
mapped to chromosomes 2-6.
Normalized Chromosome Values
Using the chromosome dose for each of the chromosomes of interest
in each of the test samples, and the mean of the corresponding
chromosome dose determined in the qualified samples of the training
set, a normalized chromosome value (NCV) was calculated using the
equation:
.mu..sigma. ##EQU00030## where {circumflex over (.mu.)}.sub.j AND
{circumflex over (.sigma.)}.sub.j are the estimated training set
mean and standard deviation respectively for the j-th chromosome
dose, and x.sub.ij is the observed j-th chromosome dose for sample
i. When chromosome doses are normally distributed, the NCV is
equivalent to a statistical z-score for the doses. No significant
departure from linearity is observed in a quantile-quantile plot of
the NCVs from unaffected samples. In addition, standard tests of
normality for the NCVs fail to reject the null hypothesis of
normality.
For the test set, an NCV was calculated for each chromosome of
interest, 21, 18, 13, X and Y for every sample. To insure a safe
and effective classification scheme, conservative boundaries were
chosen for aneuploidy classification. For classification of the
autosomes' aneuploidy state, a NCV>4.0 was required to classify
the chromosome as affected (i.e. aneuploid for that chromosome) and
a NCV<2.5 to classify a chromosome as unaffected. Samples with
autosomes that have an NCV between 2.5 and 4.0 were classified as
"no call".
Sex chromosome classification in the test was performed by
sequential application of NCVs for both X and Y as follows:
If NCV Y>-2.0 standard deviations from the mean of male samples,
then the sample was classified as male (XY).
If NCV Y<-2.0 standard deviations from the mean of male samples,
and NCV X>-2.0 standard deviations from the mean of female
samples, then the sample was classified as female (XX).
If NCV Y<-2.0 standard deviations from the mean of male samples,
and NCV X<-3.0 standard deviations from the mean of female
samples, then the sample was classified as monosomy X, i.e. Turner
syndrome.
If the NCVs did not fit into any of the above criteria, then the
sample was classified as a "no call" for sex.
Results
Study Population Demographics
A total of 1,014 patients were enrolled between April 2009 and July
2010. The patient demographics, invasive procedure type and
karyotype results are summarized in Table 19. The average age of
study participants was 35.6 yrs (range 17 to 47 yrs) and
gestational age ranged between 6 weeks, 1 day to 38 weeks, 1 day
(mean 15 weeks, 4 days). The overall incidence of abnormal fetal
chromosome karyotypes was 6.8% with T21 incidence of 2.5%. Of 946
subjects with singleton pregnancies and karyotype, 906 (96%) showed
at least one clinically recognized risk factor for fetal aneuploidy
prior to prenatal procedure. Even eliminating those with advanced
maternal age as their sole indication, the data demonstrates a very
high false positive rate for current screening modalities.
Ultrasound findings of increased nuchal translucency, cystic
hygroma, or other structural congenital abnormality by ultrasound
were most predictive of abnormal karyotype in this cohort.
TABLE-US-00019 TABLE 19 Patient Demographics Total Enrolled
Training Set Test Set (N = 1014) (N = 71) (N = 48) Dates of
Enrollment April 2009-July April 2009- January 2010- 2010 December
2009 June 2010 Number enrolled 1014 435 575 Maternal Age, yrs Mean
(SD) 35.6 (5.66) 36.4 (6.05) 34.2 (8.22) Min/Max 17/47 20/46 18/46
Not Specified, N 11 3 0 Ethnicity, N (%) Caucasian 636 (62.7) 50
(70.4) 24 (50.0) Hispanic 167 (16.5) 6 (8.5) 13 (27.0) Asian 63
(6.2) 6 (8.5) 5 (10.4) Multi, more than one 53 (5.2) 6 (8.5) 1
(2.1) African American 41 (4.0) 1 (1.3) 3 (6.3) Other 36 (3.6) 2
(2.8) 1 (2.1) Native American 9 (0.9) 0 (0.0) 1 (2.1) Not Specified
9 (0.9) 0 (0.0) 0 (0.0) Gestational Age, wks, days Mean 15 w 4 d 14
w 5 d 15 w 3 d Min/Max 6 w 1 d/38 w 10 w 0 d/23 w 10 w 4 d/28 w 1 d
1 d 3 d Number of Fetus, N 1 982 67 47 2 30 4 1 3 2 0 0 Prenatal
Procedure, N (%) CVS 430 (42.4) 38 (53.5) 28 (58.3) Amniocentesis
571 (56.3) 32 (45.1) 20 (41.7) Not specified 3 (0.3) 1 (1.4) 0
(0.0) Not performed 10 (1.0) 0 (0.0) 0 (0.0) Fetal Karyotype, N (%)
46 XX 453* (43.9) 22* (29.7) 7* (14.6) 46 XY 474* (45.9) 26* (35.1)
14 (29.2) 47, +21, both sexes 25* (2.4) 10* (13.5) 13 (27.1) 47,
+18, both sexes 14 (1.4) 5 (6.8) 8 (16.7) 47, +13, both sexes 4
(0.4) 2 (2.7) 1 (2.1) 45, X 8 (0.8) 3 (4.1) 3 (6.3) Complex, other
18* (1.7) 6 (8.1) 2 (4.2) Karyotype not 36 (3.5) 0 (0.0) 0 (0.0)
available Prenatal Screening Analyzed Risks for Karyotyped
Non-sequenced Training Analyzed Test Singletons, N (%) N = 834 N =
65 N = 47 AMA only 445 (53.4) 27 (41.5) 21 (44.7) (.gtoreq.35
years) Screen positive 149 (17.9) 18 (27.7) 9 (19.1) (trisomy)**
Increased NT 35 (4.2) 3 (4.6) 5 (10.6) Cystic Hygroma 12 (1.4) 5
(7.7) 4 (8.5) Cardiac Defect 14 (1.7) 0 (0.0) 4 (8.5) Other
Congenital 78 (9.4) 4 (6.2) 3 (6.4) Abnormality Other Maternal Risk
64 (7.7) 5 (7.7) 1 (2.1) None specified 37 (4.4) 3 (4.6) 0 (0.0)
*Includes results of fetuses from multiple gestations, **Assessed
and reported by clinicians Abbreviations: AMA = Advanced Maternal
Age, NT = nuchal translucency
The distribution of diverse ethnic backgrounds represented in this
study population is also shown in Table 19. Overall, 63% of the
patients in this study were Caucasian, 17% Hispanic, 6% Asian, 5%
multi-ethnic, and 4% African American. It was noted that the ethnic
diversity varied significantly from site to site. For example, one
site enrolled 60% Hispanic and 26% Caucasian subjects while three
clinics all located in the same state, enrolled no Hispanic
subjects. As expected, there were no discernible differences
observed in our results for different ethnicities.
Training Data Set 1
The training set study selected 71 samples from the initial
sequential accumulation of 435 samples that were collected between
April 2009 and December 2009. All subjects with affected fetus'
(abnormal karyotypes) in this first series of subjects were
included for sequencing and a random selection and number of
non-affected subjects with adequate sample and data. Clinical
characteristics of the training set patients were consistent with
the overall study demographics as shown in Table 19. The
gestational age range of the samples in the training set ranged
from 10 weeks, 0 days to 23 weeks 1 day. Thirty-eight underwent
CVS, 32 underwent amniocentesis and 1 patient did not have the
invasive procedure type specified (an unaffected karyotype 46, XY).
70% of the patients were Caucasian, 8.5% Hispanic, 8.5% Asian, and
8.5% multi-ethnic. Six sequenced samples were removed from this set
for the purposes of training: 4 samples from subjects with twin
gestations (further discussed below), 1 sample with T18 that was
contaminated during preparation, and 1 sample with a fetal
karyotype 69,XXX, leaving 65 samples for the training set.
The number of unique sequence sites (i.e. tags identified with
unique sites in the genome) varied from 2.2M in the early phases of
the training set study to 13.7M in the latter phases due to
improvements in sequencing technology over time. In order to
monitor for any potential shifts in the chromosome doses over this
6-fold range in unique sites, different unaffected samples were run
at the beginning and end of the study. For the first 15 unaffected
samples run, the average number of unique sites was 3.8M and the
average chromosome doses for chromosome 21 and chromosome 18 were
0.314 and 0.528, respectively. For the last 15 unaffected samples
run, the average number of unique sites was 10.7M and the average
chromosome doses for chromosome 21 and chromosome 18 were 0.316 and
0.529, respectively. There was no statistical difference between
the chromosome doses for chromosome 21 and chromosome 18 over the
time of the training set study.
The training set NCVs for chromosomes 21, 18 and 13 are shown in
FIG. 42. The results shown in FIG. 42 are consistent with an
assumption of normality in that roughly 99% of the diploid NCVs
would fall within .+-.2.5 standard deviations of the mean. Of this
set of 65 samples, 8 samples with clinical karyotypes indicating
T21 had NCVs ranging from 6 to 20. Four samples having clinical
karyotypes indicative of fetal T18 had NCVs ranging from 3.3 to 12,
and the two samples having karyotypes indicative of fetal trisomy
13 (T13) had NCVs of 2.6 and 4. The spread of the NCVs in affected
samples is due to their dependence on the percentage of fetal cfDNA
in the individual samples.
Similar to the autosomes, the means and standard deviations for the
sex chromosomes were established in the training set. The sex
chromosome thresholds allowed 100% identification of male and
female fetuses in the training set.
Test Data Set 1
Having established chromosome doses means and standard deviations
from the training set, a test set of 48 samples was selected from
samples collected between January 2010 and June 2010 from 575 total
samples. One of the samples from a twin gestation was removed from
the final analysis leaving 47 samples in the test set. Personnel
preparing samples for sequencing and operating the equipment were
blinded to the clinical karyotype information. The gestational age
range was similar to that seen in the training set (Table 19). 58%
of the invasive procedures were CVS, higher than that of the
overall procedural demographics, but also similar to the training
set. 50% of subjects were Caucasian, 27% Hispanic, 10.4% Asian and
6.3% African American.
In the test set, the number of unique sequence tags varied from
approximately 13M to 26M. For unaffected samples, the chromosome
doses for chromosome 21 and chromosome 18 were 0.313 and 0.527,
respectively. The test set NCVs for chromosome 21, chromosome 18
and chromosome 13 are shown in FIG. 43 and the classifications are
given in Table 20.
TABLE-US-00020 TABLE 20 Test Set Classification Data Test Set
Classification Data T21 classification Unaffected Karyotype for T21
T21 No Call Unaffected for T21 34 47, XX or XY + 21 13 T18
classification Unaffected Karyotype for T18 T18 No Call Unaffected
for T18 39 47, XX or XY + 18 8 T13 classification Unaffected
Karyotype for T13 T13 No Call Unaffected for T13 46 47, XX or XY +
13 1 Sex Chromosome Classification Karyotype XY XX MX* No Call 46,
XY 24 46, XX 18 1 45, X 2 1 Cplx 1 *MX is monosomy in the X
chromosome with no evidence of Y chromosome
In the test set, 13/13 subjects having clinical karyotypes that
indicated fetal T21 were correctly identified having NCVs ranging
from 5 to 14. Eight/eight subjects having karyotypes that indicated
fetal T18 were correctly identified having NCVs ranging from 8.5 to
22. The single sample having a karyotype classified as T13 in this
test set was classified as a no call with an NCV of approximately
3.
For the test data set, all male samples were correctly identified
including a sample with complex karyotype, 46,XY+marker chromosome
(unidentifiable by cytogenetics) (Table 11). Nineteen of twenty
female samples were correctly identified, and one female sample was
categorized as a no call. For three samples in the test set with
karyotype of 45,X, two of the three were correctly identified as
monosomy X and 1 was classified as a no call (Table 20).
Twins
Four of the samples initially selected for the training set and one
of the samples in the test set were from twin gestations. The
thresholds being employed here could be confounded by the differing
amount of cfDNA expected in the setting of a twin gestation. In the
training set, the karyotype from one of the twin samples was
monochorionic 47,XY+21. A second twin sample was fraternal and
amniocentesis was carried out on each of the fetuses individually.
In this twin gestation, one of the fetuses had a karyotype of
47,XY+21 while the other had a normal karyotype, 46,XX. In both of
these cases the cell free classification based on the methods
discussed above classified the sample as T21. The other two twin
gestations in the training set were classified correctly as
non-affected for T21 (all twins showed diploid karyotype for
chromosome 21). For the twin gestation sample in the test set,
karyotype was only established for Twin B (46,XX) and the algorithm
correctly classified as non-affected for T21.
Conclusion
The data show that massively parallel sequencing can be used to
determine a plurality abnormal fetal karyotypes from the blood of
pregnant women. These data demonstrate that 100% correct
classification of samples with trisomy 21 and trisomy 18 can be
identified using independent test set data. Even in the case of
fetuses with abnormal sex chromosome karyotypes, none of the
samples were incorrectly classified with the algorithm of the
method. Importantly, the algorithm also performed well in
determining the presence of T21 in two sets of twin pregnancies
having at least one affected fetus, which has never been shown
previously. Furthermore, this study examined a variety of
sequential samples from multiple centers representing not only the
range of abnormal karyotypes that one is likely to witness in a
commercial clinical setting, but showing the significance of
accurately classifying pregnancies non-affected by common trisomies
to address the unacceptably high false positive rates that remain
in prenatal screening today. The data provide valuable insight into
the vast capabilities of employing this method in the future.
Analysis of subsets of the unique genomic sites showed increases in
the variance consistent Poisson counting statistics.
The data build on the findings of Fan and Quake who demonstrated
that the sensitivity of noninvasive prenatal determination of fetal
aneuploidy from maternal plasma using massively parallel sequencing
is only limited by the counting statistics (Fan and Quake, PLos One
5, e10439 [2010]). Because sequencing information was collected
across the entire genome, this method is capable of determining any
aneuploidy or other copy number variation including insertions and
deletions. The karyotype from one of the samples had a small
deletion in chromosome 11 between q21 and q23 that was observed as
a .about.10% decrease in the relative number of tags in a 25 Mb
region starting at q21 when the sequencing data was analyzed in 500
kbase bins. In addition, in the training set, three of the samples
had complex sex karyotypes due to mosaicism in the cytogenetic
analysis. These karyotypes were: i) 47,XXX[9]/45,X[6], ii) 45,X
[3]/46, XY[17], and iii) 47,XXX[13]/45,X[7]. Sample ii, which
showed some XY-containing cells was correctly classified as XY.
Samples i (from CVS procedure) and iii (from amniocentesis), which
both showed a mixture of XXX and X cells by cytogenetic analysis
(consistent with mosaic Turner syndrome), were classified as a no
call and monosomy X, respectively.
In testing the algorithm, another interesting data point was
observed having an NCV between -5 and -6 for chromosome 21 for one
sample from the test set (FIG. 43). Although this sample was
diploid in chromosome 21 bp cytogenetics, the karyotype showed
mosaicism with partial triploidy for chromosome 9; 47, XX+9 [9]/46,
XX [6]. Since chromosome 9 is used in the denominator to determine
the chromosome dose for chromosome 21 (Table 18), this lowers the
overall NCV value. The ability of the use of normalizing
chromosomes to determine fetal trisomy 9 in this sample is
evidenced by the results provided in Example 13 below.
The conclusion of Fan, et al regarding the sensitivity of these
methods is only correct if the algorithms being utilized are able
to account for any random or systematic biases introduced by the
sequencing method. If the sequencing data is not properly
normalized the resulting analysis will be inferior to the counting
statistics. Chiu, et al noted in their recent paper that their
measurement of chromosomes 18 and 13 using the massively parallel
sequencing method was imprecise, and concluded that more research
was necessary to apply the method to the determination of T18 and
T13 (Chiu et al., BMJ 342:c7401 [2011]). The method utilized in the
Chiu, et al paper simply uses the number of sequence tags on the
chromosome of interest, in their case chromosome 21, normalized by
the total number of tags in the sequencing run. The challenge for
this approach is that the distribution of tags on each chromosome
can vary from sequencing run to sequencing run, and thus increases
the overall variation of the aneuploidy determination metric. In
order to compare the results of the Chiu algorithm to the
chromosome doses used in this example, the test data for
chromosomes 21 and 18 was reanalyzed using the method recommended
by Chiu, et al. as shown in FIG. 44. Overall, a compression in the
range of NCV for each of the chromosomes 21 and 18 was observed as
well as a decrease in the determination rate with 10/13 T21 and 5/8
of the T18 samples correctly identified from our test set utilizing
an NCV threshold of 4.0 for aneuploidy classification.
Ehrich, et al also focused only on T21 and used the same algorithm
as Chiu, et al., (Ehrich et al., Am J Obstet Gynecol 204:205 e1-e11
[2011]). In addition, after observing a shift in their test set
z-score metric from the external reference data i.e. training set,
they retrained on the test set to establish the classification
boundaries. Although in principle this approach is feasible, in
practice it would be challenging to decide how many samples are
required to train and how often one would need to retrain to ensure
that the classification boundaries are correct. One method of
mitigating this issue is to include controls in every sequencing
run that measure the baseline and calibrate for quantitative
behavior.
The data obtained using the present method show that massively
parallel sequencing is capable of determining multiple fetal
chromosomal abnormalities from the plasma of pregnant women when
the algorithm for normalizing the chromosome counting data is
optimized. The present method for quantification not only minimizes
random and systematic variations between sequencing runs, but also
allows for effective classification of aneuploidies across the
entire genome, most notably T21 and T18. Larger sample collections
are required to test the algorithm for T13 determination. To this
end, a prospective, blinded, multi-site clinical study to further
demonstrate the diagnostic accuracy of the present method is being
performed.
Example 13
Determination of the Presence or Absence of at Least 5 Different
Chromosomal Aneuploidies in All Chromosomes of Individual Test
Samples
To demonstrate the capability of the method to determine the
presence or absence of any chromosomal aneuploidy in each of a set
of maternal test samples (test set 1; Example 12), systematically
determined normalizing chromosome sequences were identified in
unaffected samples of the training set (training set 1; Example
12), and used to calculate chromosome doses for all chromosomes in
each of the test samples. Determination of the presence or absence
of any one or more different complete fetal chromosomal
aneuploidies in each of the test and training set samples was
accomplished from sequencing information obtained from a single
sequencing run on each individual sample.
Using the chromosome densities i.e. the number of sequence tags
identified for each chromosome in each of the samples of the
training set described in Example 12, a systematically determined
normalizing chromosome sequence consisting of a single chromosome
or a group of chromosomes was determined by calculating a single
chromosome dose for each of chromosomes 1-22, X and Y. The
systematically determined normalizing chromosome sequence for each
of chromosomes 1-22, X, and Y was determined by systematically
calculating chromosome doses for each chromosome using every
possible combination of chromosomes as the denominator. For
example, for chromosome 21 as the chromosome of interest,
chromosome doses were calculated as a ratio of (i) the number of
sequence tags obtained for chromosome 21 (chromosome of interest)
and (ii) the number of sequence tags obtained for each of the
remaining chromosomes, and the sum of the number of tags obtained
for all possible combinations of the remaining chromosomes
(excluding chromosome 21) i.e. 1, 2, 3, 4, 5, etc. up to 20, 21,
22, X, and Y; 1+2, 1+3, 1+4, 1+5, etc. up to 1+20, 1+22, 1+X, and
1+Y; 1+2+3, 1+2+4, 1+2+5 etc. up to 1+2+20, 1+2+22, 1+2+X, and
1+2+Y; 1+3+4, 1+3+5, 1+3+6 etc. up to 1+3+20, 1+3+22, 1+3+X, and
1+3+Y; 1+2+3+4, 1+2+3+5, 1+2+3+6 etc. up to 1+2+3+20, 1+2+3+22,
1+2+3+X, and 1+2+3+Y; and so on such that all possible combinations
of all of chromosomes 1-20, 22, X and Y were used as a normalizing
chromosome sequence (denominator) to determine all possible
chromosome doses for each chromosome of interest in each of the
qualified (aneuploid) samples in the training set. Chromosome doses
were determined in the same manner for chromosome 21 in all
training samples, and the systematically determined normalizing
chromosome sequence for chromosome 21 was determined as the single
or group of chromosomes resulting in a dose for chromosome 21
having the smallest variability across all training samples. The
same analysis was repeated to determine the single chromosome or
combination of chromosomes that would serve as the systematically
determined normalizing chromosome sequence for each of the
remaining chromosomes including chromosomes 13, 18, X and Y i.e.
all possible combinations of chromosomes were used to determine the
normalizing sequence (single chromosome or a group of chromosomes)
for all other chromosomes of interest 1-12, 14-17, 19-20, 22, X and
Y, in all training samples. Thus, all chromosomes were treated as
chromosomes of interest, and a systematically determined
normalizing sequence was determined for each of all chromosomes in
each of the unaffected samples in the training set. Table 21
provides the single or the group of chromosomes that were
identified as the systematically determined normalizing sequence
for each of chromosomes of interest 1-22, X, and Y. As highlighted
by Table 21, for some chromosomes of interest, the systematically
determined normalizing chromosome sequence was determined to be a
single chromosome (e.g. when chromosome 4 is the chromosome of
interest), and for other chromosomes of interest, the
systematically determined normalizing chromosome sequence was
determined to be a group of chromosomes (e.g. when chromosome 21 is
the chromosome of interest).
TABLE-US-00021 TABLE 21 Systematically Determined Normalizing
Chromosome Sequences for All Chromosomes Chromosome Systematically
of Determined Interest Normalizing Sequence 1 6 + 10 + 14 + 15 + 17
+ 20 2 3 + 6 + 8 + 9 + 10 3 2 + 4 + 5 + 6 + 12 4 5 5 4 + 6 + 8 + 14
6 3 + 4 + 5 + 12 + 14 7 4 + 5 + 8 + 14 + 19 + 20 8 2 + 5 + 7 9 3 +
4 + 8 + 10 + 17 + 19 + 20 + 22 10 2 + 14 + 15 + 17 + 20 11 5 + 10 +
14 + 20 + 22 12 1 + 2 + 3 + 5 + 6 + 19 13 4 + 5 14 1 + 3 + 5 + 6 +
10 + 19 15 1 + 14 + 20 16 14 + 17 + 19 + 20 + 22 17 15 + 19 + 22 18
2 + 3 + 5 + 7 19 22 20 10 + 16 + 17 + 22 21 4 + 14 + 16 + 20 + 22
22 19 X 4 + 8 Y 4 + 6
The mean, standard deviation (SD) and coefficient of variance (CV)
for the systematically determined normalizing chromosome sequence
determined for each of all chromosomes are given in Table 22.
TABLE-US-00022 TABLE 22 Mean, Standard Deviation and Coefficient of
Variance for all systematically determined normalizing chromosome
sequences Chromosome of interest Mean SD CV 1 0.36637 0.00266 0.72%
2 0.31580 0.00068 0.22% 3 0.21983 0.00055 0.18% 4 0.98191 0.02509
2.56% 5 0.30109 0.00076 0.25% 6 0.21621 0.00059 0.27% 7 0.21214
0.00044 0.21% 8 0.25562 0.00068 0.27% 9 0.12726 0.00034 0.27% 10
0.24471 0.00098 0.40% 11 0.26907 0.00098 0.36% 12 0.12358 0.00029
0.23% 13.sup.a 0.26023 0.00122 0.47% 14 0.09286 0.00028 0.30% 15
0.21568 0.00147 0.68% 16 0.25181 0.00134 0.53% 17 0.46000 0.00248
0.54% 18.sup.a 0.10100 0.00038 0.38% 19 1.43709 0.02899 2.02% 20
0.19967 0.00123 0.62% 21.sup.a 0.07851 0.00053 0.67% 22 0.69613
0.01391 2.00% X.sup.b 0.46865 0.00279 0.68% Y.sup.b 0.00028 0.00004
14.97% .sup.aExcluding trisomies .sup.bFemale fetus
The variance in chromosome doses across all training samples as
reflected by the value of the CV, substantiates the use of
systematically determined normalizing chromosome sequences to
provide a large signal-to-noise ratio and dynamic range, allowing
for the determination of the aneuploidies to be made with high
sensitivity and high specificity, as shown in the following.
To demonstrate the sensitivity and specificity of the method,
chromosome doses for all chromosomes of interest 1-22, X and Y were
determined in each of the samples in the training set, and in each
of all samples in the test set described in Example 11 using the
corresponding systematically determined normalizing chromosome
sequences provided in Table 21 above.
Using the systematically determined normalizing chromosome sequence
for each of the chromosomes of interest, the presence or absence of
any chromosomal aneuploidy was determined in each of the samples in
the training set, and in each of the test samples i.e. it was
determined whether each sample contained a complete fetal
chromosomal aneuploidy of chromosome 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, and Y. Sequence
information i.e. the number of sequence tags, was obtained for all
chromosomes in each of the samples in the training set, and in each
of the test samples, and a single chromosome dose for each of the
chromosomes in each of the training and test samples was calculated
as described above using the number of sequence tags obtained for
the systematically determined normalizing chromosome sequences
corresponding to those determined in the trained set (Table 21).
The number of sequence tags obtained in each of the training
samples for the systematically determined normalizing chromosome
sequences was used to determine the chromosome doses for each
chromosome in each of the training samples, and the number of
sequence tags obtained in each of the test samples for the
systematically determined normalizing chromosome sequence was used
to determine the chromosome dose for each chromosome for each of
the test samples. To ensure safe and effective classification of
aneuploidies, the same conservative boundaries were chosen as
described in Example 12.
Training Set Results
A plot of the chromosome doses for chromosomes 21, 18 and 13 in the
training set of samples using the systematically determined
normalizing chromosome sequence is given in FIG. 45. When using the
systematically determined normalizing chromosome sequence i.e. the
group of chromosomes 4+14+16+20+22, 8 samples with clinical
karyotypes indicating T21 had NCVs between 5.4 and 21.5. When using
the systematically determined normalizing chromosome sequence i.e.
the group of chromosomes 2+3+5+7, 4 samples with clinical
karyotypes indicating T18 had NCVs between 3.3 and 15.3. When using
the systematically determined normalizing chromosome sequence i.e.
the group of chromosomes 4+5, 2 samples with clinical karyotypes
indicating T13 had NCVs of 8.0 and 12.4. The T21 samples of the
training set are shown as the last 8 samples of the chromosome 21
data (O); the T18 samples of the training set are shown as the last
4 samples of the chromosome 18 data (.DELTA.); and the T13 samples
of the training set are shown as the last 2 samples of the
chromosome 13 data (.quadrature.).
These data show that normalizing chromosome sequences can be used
to determine and correctly classify different complete fetal
chromosomal aneuploidies with great confidence. Since all samples
with affected karyotypes had NCVs greater than 3, there is less
than approximately 0.1% probability that these samples are part of
the unaffected distribution.
Similarly to the autosomes, when the systematically determined
normalizing chromosome sequence (i.e. the group of chromosomes 4+8)
was used for chromosome X, and when the systematically determined
normalizing chromosome sequence (i.e. the group of chromosomes 4+6)
was used for chromosome Y, all of the male and female fetuses in
the training set were correctly identified. In addition, all 5 of
the monosomy X samples were identified. FIG. 46A shows a plot of
NCVs determined for the X chromosome (X-axis) and NCVs determined
for the Y chromosome (Y axis) for each of the samples in the
training set. All of the samples which are monosomy e X by
karyotype have NCV values of less than -4.83. Those monosomy X
samples that have karyotypes consistent with a 45,X karyotype (full
or mosaic) have a Y NCV value close to zero as expected. Female
samples cluster around NCV=0 for both X and Y.
Test Set Results
A plot of the chromosome doses for chromosomes 21, 18 and 13 in the
test samples using the relevant systematically determined
normalizing chromosome sequences is given in FIG. 47. When using
the systematically determined normalizing chromosome sequence (i.e.
the group of chromosomes 4+14+16+20+22), then 13 of 13 samples with
clinical karyotypes indicating T21 were correctly identified with
NCVs between 7.2 and 16.3. When using the systematically determined
normalizing chromosome sequence (i.e. the group of chromosomes
2+3+5+7), then all 8 samples with clinical karyotypes indicating
T18 were identified with NCVs between 12.7 and 30.7. When using the
systematically determined normalizing chromosome sequence (i.e. the
group of chromosomes 4+5), then the only one sample with clinical
karyotypes indicating T13 was correctly identified with an NCV of
8.6. The T21 samples of the test set are shown as the last 13
samples of the chromosome 21 data (O); the T18 samples of the test
set are shown as the last 8 samples of the chromosome 18 data
(.DELTA.); and the T13 sample of the test set is shown as the last
sample of the chromosome 13 data (.quadrature.).
These data show that systematically determined normalizing
chromosome sequences can be used to determine and correctly
classify different complete fetal chromosomal aneuploidies with
great confidence. Similar to the training set, all samples with
affected karyotypes had NCVs greater than 7, which indicated an
infinitesimally small probability that these samples are part of
the unaffected distribution (FIG. 47).
Similarly to the autosomes, when the systematically determined
normalizing chromosome sequence (i.e. the group of chromosomes 4+8)
was used for chromosome X, and when the systematically determined
normalizing chromosome sequence (i.e. the group of chromosomes 4+6)
was used for chromosome Y, all of the male and female fetuses in
the test set were correctly identified. In addition, all 3 of the
monosomy X samples were determined. FIG. 46B shows a plot of NCVs
determined for the X chromosome (X-axis) and NCVs determined for
the Y chromosome (Y axis) for each of the samples in the test
set.
As previously described, the present method allows for determining
the presence or absence of a complete, or partial, chromosomal
aneuploidy of each of chromosomes 1-22, X, and Y in each sample. In
addition to determining complete chromosomal aneuploidies T13, T18,
T21, and monosomy X, the method determined the presence of a
trisomy of chromosome 9 in one of the test samples. When using the
systematically determined normalizing chromosome sequence (i.e. the
group of chromosomes 3+4+8+10+17+19+20+22), for chromosome of
interest 9, a sample having an NCV of 14.4 was identified (FIG.
48). This sample corresponded to the test sample in Example 12 that
was suspected of being aneuploid for chromosome 9 following the
calculation of an aberrantly low dose for chromosome 21 (for which
chromosome 9 was used as the normalizing chromosome sequence in
Example 12).
The data show that 100% of the samples having clinical karyotypes
indicating T21, T13 T18, T9 and monosomy X were correctly
identified. FIG. 49 shows a plot of the NCVs for each of
chromosomes 1-22 in each of the 47 test samples. Medians of NCVs
were normalized to zero. The data show that the method of the
invention (including the use of systematically determined
normalizing chromosome sequences) determined the presence of all 5
types of chromosomal aneuploidies that were present in this test
set with 100% sensitivity and 100% specificity, and clearly
indicate that the method can identify any complete chromosomal
aneuploidy for any one of chromosomes 1-22, X, and Y, in any
sample.
Example 14
Determination of the Presence or Absence of a Partial Fetal
Chromosomal Aneuploidy
Determination of Cat Eye Syndrome
DiGeorge syndrome (22q11.2 deletion syndrome), a disorder caused by
a defect in chromosome 22, results in the poor development of
several body systems. Medical problems commonly associated with
DiGeorge syndrome include heart defects, poor immune system
function, a cleft palate, poor function of the parathyroid glands
and behavioral disorders. The number and severity of problems
associated with DiGeorge syndrome vary greatly. Almost everyone
with DiGeorge syndrome needs treatment from specialists in a
variety of fields.
To determine the presence or absence of a partial deletion of fetal
chromosome 22, a blood sample is obtained by venipuncture for the
mother, and cfDNA is prepared as described in the Examples above.
The purified cfDNA is ligated to adaptors and subjected to cluster
amplification using the Illumina cBot cluster station. Massively
parallel sequencing is performed using reversible dye terminators
to generate millions of 36 bp reads. The sequence reads are aligned
to the human hg19 reference genome, and the reads that are uniquely
mapped to the reference genome are counted as tags.
A set of qualified samples all known to be diploid for chromosome
22 i.e. chromosome 22 or any portion thereof is known to be present
only in a diploid state, are first sequenced and analyzed to obtain
a number of sequence tags for each of 1000 segments of 3 megabases
(Mb) (excluding the region 22q11.2). Given that the human genome
comprises approximately 3 billion bases (3 Gb), the 1000 segments
of 3 Mb each approximately composes the remainder of the genome.
Each of the 1000 segments can serve individually or as in a group
of segment sequences that are used to determine the normalizing
segment sequence for the segment of interest i.e. the 3 Mb region
of 22q11.2. The number of sequence tags mapped to every single 1000
bp segment is used individually to compute segment doses for the 3
Mb region of 22q11.2. In addition, all possible combinations of two
or more segments are used to determine segment doses for the
segment of interest in all qualified samples. The single 3 Mb
segment or the combination of two or more 3 Mb segments that result
in the segment dose having the lowest variability across samples is
chosen as the normalizing segment sequence.
The number of sequence tags mapped to the segment of interest in
each of the qualified samples is used to determine a segment dose
in each of the qualified samples. The mean and standard deviation
of the segment doses in all qualified samples is calculated, and
used to set threshold s to which segment doses determined in test
samples can be compared. Preferably, normalized segment values
(NSV) are calculated for all segments of interest in all qualified
samples, and used to set the threshold values.
Subsequently, the number of tags mapped to the normalizing segment
sequence in the corresponding test sample is used to determine the
dose of the segment of interest in the test sample. A normalized
segment value (NSV) is calculated for the segment in the test
sample as described previously and the NCV of the segment of
interest in the test sample is compared to the threshold determined
using the qualified samples to determine the presence or absence of
a deletion of 22q11.2 in the test sample.
A test NCV<-3, indicates that a loss in the segment of interest
i.e. partial deletion of chromosome 22 (22q11.2) is present in the
test sample.
Example 15
Stool DNA Testing for Prediction of Outcome for Stage II Colorectal
Cancer Patients
Around 30% of all stage II colon cancer patients will relapse and
die of their disease. Stage II colon cancers of patients who had
relapse of disease showed significantly more losses on chromosomes
4, 5, 15q, 17q and 18q. In particular, stage 11 colon cancer
patients losses on 4q22.1-4-q35.2 have been shown to be associated
with worse outcome. Determination of the presence or absence of
these genomic alterations may aid in selecting patients for
adjuvant therapy (Brosens et al., Analytical Cellular
Pathology/Cellular Oncology 33: 95-104 [2010]).
To determine the presence or absence of one or more chromosomal
deletions in the 4q22.1 to 4q35.2 region in patients with stage II
colorectal cancer, stool and/or plasma samples are obtained from
the patient(s). Stool DNA is prepared according to the method
described by Chen et al., J Natl Cancer Inst 97:1124-1132 [2005]);
and plasma DNA is prepared according to the method described in the
Examples above. DNA is sequenced according to an NGS method
described herein, and the sequence information for the patient(s)
sample(s) is used to calculate segment doses for one or more
segments spanning the 4q22.1 to 4q35.2 region. Segment doses are
determined using normalizing segment sequences that are determined
a priori by in a set of qualified stool and/or plasma samples,
respectively. Segment doses in the test samples (patient samples)
are calculated, and the presence or absence of one or more partial
chromosomal deletions within the 4q22.1 to 4q35.2 region is
determined by comparing the NSV for each of the segments of
interest to the threshold set from the NSV in the set of qualified
samples.
Example 16
Genome Wide Fetal Aneuploidy Detection by Sequencing of Maternal
Plasma DNA
Diagnostic Accuracy in a Prospective, Blinded, Multicenter
Study
The method for determining the presence or absence of aneuploidies
in maternal test samples was used in a prospective study, and its
diagnostic accuracy was shown as described below. The prospective
study further demonstrates the efficacy of the method of the
invention to detect fetal aneuploidy for multiple chromosomes
across the genome. The blinded study emulates an actual population
of pregnant women in which the fetal karyotype is unknown, and all
samples with any abnormal karyotypes were selected for sequencing.
Determinations of the classifications made according to the method
of the invention were compared to fetal karyotypes from invasive
procedures to determine the diagnostic performance of the method
for multiple chromosomal aneuploidies.
Summary of this Example.
Blood samples were collected in a prospective, blinded study from
2,882 women undergoing prenatal diagnostic procedures at 60 United
States sites (clinicaltrials.gov NCT01122524).
An independent biostatistician selected all singleton pregnancies
with any abnormal karyotype, and a balanced number of randomly
selected pregnancies with euploid karyotypes. Chromosome
classifications were made for each sample according the method of
the invention and compared to fetal karyotype.
Within an analysis cohort of 532 samples, 89/89 trisomy 21 cases,
(sensitivity 100% (95% CI 95.9-100)), 35/36 trisomy 18 cases
(sensitivity 97.2%, (95% CI 85.5-99.9)), 11/14 trisomy 13 cases
(sensitivity 78.6%, (95% CI 49.2-99.9)), 232/233 females
(sensitivity 99.6%, (95% CI 97.6->99.9)), 184/184 males
(sensitivity 100%, 95% CI 98.0-100)), and 15/16 monosomy X cases
(sensitivity 93.8%, 95% CI 69.8-99.8)) were classified. There were
no false positives for autosomal aneuploidies in unaffected
subjects (100% specificity, (95% CI>98.5-100)). In addition,
fetuses with mosaicism for trisomy 21 (3/3), trisomy 18 (1/1), and
monosomy X (2/7), three cases of translocation trisomy, two cases
of other autosomal trisomies (20 and 16) and other sex chromosome
aneuploidies (XXX, XXY and XYY) were correctly classified.
The results further demonstrate the efficacy of the present method
to detect fetal aneuploidy for multiple chromosomes across the
genome using maternal plasma DNA. The high sensitivity and
specificity for the detection of trisomies 21, 18, 13 and monosomy
X suggest that the present method can be incorporated into existing
aneuploidy screening algorithms to reduce unnecessary invasive
procedures.
Materials and Methods
The MELISSA (MatErnal BLood IS Source to Accurately diagnose fetal
aneuploidy) study was conducted as a prospective, multi-center
observational study with blinded nested case: control analyses.
Pregnant women, 18 years and older undergoing an invasive prenatal
procedure to determine fetal karyotype were recruited
(Clinicaltrials.gov NCT01122524). Eligibility criteria included
pregnant women between 8 weeks, 0 days and 22 weeks, 0 days
gestation who met at least one of the following additional
criteria: age .gtoreq.38 years, positive screening test result
(serum analytes and/or nuchal translucency (NT) measurement),
presence of ultrasound markers associated with increased risk for
fetal aneuploidy, or prior aneuploid fetus. Written informed
consent was obtained from all women who agreed to participate.
Enrollment occurred at 60 geographically dispersed medical centers
in 25 states per protocol approved by institutional review boards
(IRB) at each institution. Two clinical research organizations
(CROs) (Quintiles, Durham, N.C. and Emphusion, San Francisco,
Calif.) were retained to maintain study blinding and provide
clinical data management, data monitoring, biostatistics, and data
analysis services.
Before any invasive procedure, a peripheral venous blood sample (17
mL) was collected in two acid citrate dextrose (ACD) tubes (Becton
Dickinson) that were de-identified and labeled with a unique study
number. Site research personnel entered study number, date, and
time of blood draw into a secure electronic case report form
(eCRF). Whole blood samples were shipped overnight in
temperature-controlled containers from sites to the laboratory
(Verinata Health, Inc., CA). Upon receipt and sample inspection,
cell-free plasma was prepared per previously described methods (see
Example 13) and stored frozen at -80.degree. C. in 2 to 4 aliquots
until time of sequencing. Date and time of sample receipt at the
laboratory were recorded. A sample was determined to be eligible
for analysis if it was received overnight, was cool to touch, and
contained at least 7 mL blood. Samples that were eligible at
receipt were reported to the CRO weekly and used for selection on a
random sampling list (see below and FIG. 50). Clinical data from
the woman's current pregnancy and fetal karyotype were entered into
the eCRF by site research personnel and verified by CRO monitors
through source document review.
Sample size determination was based on the precision of the
estimates for a targeted range of performance characteristics
(sensitivity and specificity) for the index test. Specifically, the
number of affected (T21, T18, T13, male, female, or monosomy X)
cases and unaffected (non-T21, non-T18, non-T13, not male, not
female, or not monosomy X) controls were determined to estimate the
sensitivity and specificity, respectively, to within a
pre-specified small margin of error based on the normal
approximation (N=(1.96 p(1-p)/margin of error).sup.2, where p=the
estimate of the sensitivity or specificity). Assuming a true
sensitivity of 95% or greater, a sample size between 73 to 114
cases ensured that the precision of the estimate of sensitivity
would be such that the lower bound of the 95% confidence interval
(CI) would be 90% or greater (margin of error .ltoreq.5%). For
smaller sample sizes, a larger estimated margin of error of the 95%
CI for sensitivity was projected (from 6% to 13.5%). To estimate
the specificity with greater precision a larger number of
unaffected controls (.about.4:1 ratio to cases) were planned at the
sampling stage. This ensured the precision of the estimate of
specificity to at least 3%. Accordingly, as the sensitivity and/or
specificity increased, the precision of the confidence interval
would also increase.
Based on sample size determination, a random sampling plan was
devised for the CRO to generate lists of selected samples to
sequence (minimum of 110 cases affected by T21, T18, or T13 and 400
non-affected for trisomy, allowing up to half of these to have
karyotypes other than 46,XX or 46,XY). Subjects with a singleton
pregnancy and an eligible blood sample were eligible for selection.
Subjects with ineligible samples, no karyotype recorded, or a
multiple gestation were excluded (FIG. 50). Lists were generated on
a regular basis throughout the study and sent to the Verinata
Health laboratory.
Each eligible blood sample was analyzed for six independent
categories. The categories were aneuploidy status for chromosomes
21, 18 and 13, and gender status for male, female and monosomy X.
While still blinded, one of three classifications (affected,
unaffected, or unclassified) were generated prospectively for each
of the six independent categories for each plasma DNA sample. Using
this scenario, the same sample could be classified as affected in
one analysis (e.g., aneuploidy for chromosome 21) and unaffected
for another analysis (e.g., euploid for chromosome 18).
Conventional metaphase cytogenetic analysis of cells obtained by
chorionic villus sampling (CVS) or amniocentesis was used as the
reference standard in this study. Fetal karyotyping was performed
in diagnostic laboratories routinely used by the participating
sites. If after enrollment a patient underwent both CVS and
amniocentesis, karyotype results from amniocentesis were used for
study analysis. Fluorescence in situ hybridization (FISH) results
for targeting chromosomes 21, 18, 13, X, and Y was allowed if a
metaphase karyotype was not available (Table 24). All abnormal
karyotype reports (i.e. other than 46, XX and 46, XY) were reviewed
by a board-certified cytogeneticist and classified as affected or
unaffected with respect to chromosomes 21, 18, and 13 and gender
status for XX, XY and monosomy X.
Pre-specified protocol conventions defined the following abnormal
karyotypes to be assigned a status of `censored` for karyotype by
the cytogeneticist: triploidy, tetraploidy, complex karyotypes
other than trisomy (e.g., mosaicism) that involved chromosomes 21,
18, or 13, mosaics with mixed sex chromosomes, sex chromosome
aneuploidy or karyotypes that could not be fully interpreted by the
source document (e.g. marker chromosomes of unknown origin). Since
the cytogenetic diagnosis was not known to the sequencing
laboratory, all cytogenetically censored samples were independently
analyzed and assigned a classification determined using sequencing
information according to the method of the invention (Sequencing
Classification), but were not included in the statistical analysis.
Censored status pertained only to the relevant one or more of the
six analyses (e.g., a mosaic T18 would be censored from chromosome
18 analysis, but considered `unaffected` for other analyses, such
as chromosomes 21, 13, X, and Y) (Table 25). Other abnormal and
rare complex karyotypes, which could not be fully anticipated at
the time of protocol design, were not censored from analysis (Table
26).
The data contained in the eCRF and clinical database were
restricted to authorized users only (at the study sites, CROs, and
contract clinical personnel). It was not accessible to any
employees at Verinata Health until the time of unblinding.
After receiving random sample lists from the CRO, total cell-free
DNA (a mixture of maternal and fetal) was extracted from thawed
selected plasma samples as described in Example 13. Sequencing
libraries were prepared utilizing the Illumina TruSeq kit v2.5.
Sequencing was carried out (6-plex--i.e. 6 samples/lane) was
performed on an Illumina HiSeq 2000 instrument in the Verinata
Health laboratory. Single-end reads of 36 base pairs were obtained.
The reads were mapped across the genome, and the sequence tags on
each chromosome of interest were counted and used to classify the
sample for independent categories as described above.
The clinical protocol required evidence of fetal DNA presence in
order to report a classification result. A classification of male
or aneuploid was considered sufficient evidence of fetal DNA. In
addition, each sample was also tested for the presence of fetal DNA
using two allele specific methods. In the first method, the
AmpflSTR Minifiler kit (Life Technologies, San Diego, Calif.) was
used to interrogate the presence of a fetal component in the cell
free DNA. Electrophoresis of short tandem repeat (STR) amplicons
was carried out on the ABI 3130 Genetic Analyzer following
manufacturer's protocols. All nine STR loci in this kit were
analyzed by comparing the intensity of each peak reported as a
percentage of the sum of the intensities of all peaks, and the
presence of minor peaks was used to provide evidence of fetal DNA.
In cases in which no minor STR could be identified, an aliquot of
the sample was examined with a single nucleotide polymorphism (SNP)
panel of 15 SNPs with average heterozygosity .gtoreq.0.4 selected
from the Kidd et al. panel (Kidd et al., Forensic Sci Int
164(1):20-32 [2006]). Allele specific methods that can be used to
detect and/or quantify fetal DNA in maternal samples are described
in U.S. Patent Publications 20120010085, 20110224087, and
20110201507, which are herein incorporated by reference.
Normalized chromosome values (NCVs) were determined by calculating
all possible permutations of denominators for all autosomes and sex
chromosomes as described in Example 13, however, because the
sequencing is this study was carried out on a different instrument
than our previous work with multiple samples/lane, new normalizing
chromosome denominators had to be determined. The normalizing
chromosome denominators in the current study were determined based
on a training set of 110 independent (i.e. not from MELISSA
eligible samples) unaffected samples (i.e. qualified samples)
sequenced prior to analysis of the study samples. The new
normalizing chromosomes denominators were determined by calculating
all possible permutations of denominators for all autosomes and sex
chromosomes that minimized the variation for the unaffected
training set for all chromosomes across the genome (Table 23).
The NCV rules that were applied to provide the autosome
classification of each test sample were those described in Example
12, i.e. for classification of aneuploidies of autosomes, a
NCV>4.0 was required to classify the chromosome as affected
(i.e. aneuploid for that chromosome) and a NCV<2.5 to classify a
chromosome as unaffected. Samples with autosomes that have an NCV
between 2.5 and 4.0 were named "unclassified".
Sex chromosome classification in the present test was performed by
sequential application of NCVs for both X and Y as follows:
1. If NCV X<-4.0 AND NCV Y<2.5, then the sample was
classified as monosomy X.
2. If NCV X>-2.5 AND NCV X<2.5 AND NCV Y<2.5, then the
sample was classified as female (XX).
3. If NCV X>4.0 AND NCV Y<2.5, then the sample was classified
as XXX.
4. If NCV X>-2.5 AND NCV X<2.5 AND NCV Y>33, then the
sample was classified as XXY.
5. If NCV X<-4.0 AND NCV Y>4.0, then the sample was
classified as male (XY).
6. If condition 5 was met, but NCV Y was approximately 2 times
greater than expected for the measured NCV X value, then the sample
was classified as XYY.
7. If the chromosome X and Y NCVs did not fit into any of the above
criteria, then the sample was classified as unclassified for
sex.
Because the laboratory was blinded to the clinical information, the
sequencing results were not adjusted for any of the following
demographic variables: maternal body mass index, smoking status,
presence of diabetes, types of conception (spontaneous or
assisted), prior pregnancies, prior aneuploidy, or gestational age.
Neither maternal nor paternal samples were utilized for
classification, and the classifications according to the present
method did not depend on the measurement of specific loci or
alleles.
The sequencing results were returned to an independent contract
biostatistician prior to unblinding and analysis. Personnel at the
study sites, CROs (including the biostatistician generating random
sampling lists) and the contract cytogeneticist were blinded to
sequencing results.
TABLE-US-00023 TABLE 23 Systematically Determined Normalizing
Chromosome Sequences for All Chromosomes Chromosome Systematically
of Determined Normalizing Interest Sequence 1 6 + 10 + 14 + 15 + 17
+ 22 2 1 + 3 + 4 + 6 + 8 + 9 + 10 3 +5 + 6 + 10 + 12 4 5 5 3 + 4 +
8 + 12 6 2 + 3 + 4 + 14 7 3 + 4 + 6 + 8 + 14 + 16 + 19 8 5 + 6 + 10
9 1 + 2 + 5 + 7 + 8 + 11 + 14 + 15 + 16 + 17 + 22 10 2 + 9 + 15 +
16 + 20 11 2 + 8 + 9 + 14 + 16 + 19 + 20 12 1 + 3 + 5 + 6 + 8 + 15
+ 19 13 4 + 6 14 1 + 3 + 4 + 5 + 9 + 11 + 15 + 17 15 1 + 10 + 20 16
20 17 15 + 19 + 22 18 5 + 8 19 22 20 15 + 16 + 17 + 22 21 4 + 17 +
22 22 19 X 4 + 5 + 8 Y 4
Statistical methods were documented in a detailed statistical
analysis plan for the study. Point estimates for sensitivity and
specificity along with exact 95% confidence intervals using the
Clopper-Pearson method were computed for each of the six analysis
categories. For all statistical estimation procedures performed,
samples with no fetal DNA detected, `censored` for complex
karyotype (per protocol-defined conventions), or `unclassified` by
the sequencing test were removed.
Results
Between June 2010 and August 2011, 2,882 pregnant women were
enrolled in the study. The characteristics of the eligible subjects
and the selected cohort are given in Table 24. Subjects that
enrolled and provided blood, but were later found during data
monitoring to exceed inclusion criteria and have an actual
gestational age at enrollment beyond 22 weeks, 0 days were allowed
to remain in the study (n=22) Three of these samples were in the
selected set. FIG. 50 shows the flow of samples between enrollment
and analysis. There were 2,625 samples eligible for selection.
TABLE-US-00024 TABLE 24 Patient Demographics Affected Eligible
Patients Analyzed Patients Patients (n = 2882) (n = 534) (n = 221)
Maternal Age, yrs Mean (SD) 35.8 (5.93) 35.2 (6.40) 34.4 (6.73)
Min/Max 18/49 18/46 18/46 Multiparous, N (%) 2348 (81.5) 425 (79.5)
176 (79.6) Pregnancy by Assisted 247 (8.6) 38 (7.1) 17 (7.7)
Reproductive Techniques, N (%) Race, N (%) White 2078 (72.1) 388
(72.7) 161 (72.9) African American 338 (11.7) 58 (10.9) 28 (12.7)
Asian 271 (9.4) 53 (9.9) 18 (8.1) American Indian or Alaska Native
22 (0.8) 5 (0.9) 2 (0.9) Multi-racial 173 (6.0) 30 (5.6) 12 (5.4)
BMI (kg/m.sup.2) Mean (SD) 26.6 (5.89) 26.2 (5.73) 26.2 (5.64)
Min/Max 15/76 17/59 18/56 Current Smoker, N (%) 165 (5.7) 29 (5.4)
6 (2.7) Maternal Diabetes Mellitus, N 61 (2.1) 11 (2.1) 6 (2.7) (%)
Trimester First 832 (28.9) 165 (30.9) 126 (57.0) Second 2050 (71.1)
369 (69.1) 95 (43.0) Gestational Age (GA)*, wks, days Mean 15.5
(3.27) 15.1 (3.16) 14.8 (3.18) Min/Max 8/31 10/23 10/23 Karyotype
Source, N (%) CVS 1044 (36.8) 228 (42.7) 121 (54.8) Amniocentesis
1783 (62.8) 301 (56.4) 95 (43.0) Products of Conception 10 (0.4) 5
(0.9) 5 (2.2) Amniocentesis after CVS, N 7 (0.2) 1 (0.2) 0 (0.0)
(%) Karyotype by FISH-only, N (%) 105 (3.6) 18 (3.4) 13 (5.9)
Number of Fetuses 1 2797 (97.1) 534 (100.0) 221 (100.0) 2 76 (2.6)
0 (0.0) 0 (0.0) 3 7 (0.2) 0 (0.0) 0 (0.0) 4 2 (0.2) 0 (0.0) 0 (0.0)
Prenatal Risk, N (%) AMA only (.gtoreq.38 years) 1061 (36.8) 152
(28.5) 21 (9.5) Positive screen risk 622 (21.6) 91 (17.0) 14 (6.3)
Ultrasound abnormality 477 (6.6) 122 (22.8) 81 (36.7)** Prior
aneuploidy pregnancy 82 (2.8) 15 (2.8) 4 (1.8) More than 1 risk 640
(22.2) 154 (28.9) 101 (45.7)** Screening Risk Estimated By, N 1749
310 125 (%) Nuchal Translucency measure 179 (10.2) 53 (17.1) 36
(28.8) alone First Trimester Combined 677 (38.7) 117 (37.7) 47
(37.6) Second Trimester Triple or 414 (23.7) 72 (23.3) 16 (12.8)
Quadruple Fully Integrated (1.sup.st and 2.sup.nd 137 (7.8) 14
(4.5) 3 (2.4) Trimester) Sequential 218 (12.5) 32 (10.3) 15 (12.0)
Other 124 (7.1) 22 (7.1) 8 (6.4) Abnormal Fetal Ultrasound, N (%)
One or more Soft Marker 837 (29.0) 242 (45.3) 166 (75.1)** One or
more Major Marker 719 (24.9) 212 (39.7) 143 (64.7) IUGR
(<10.sup.th percentile) 228 (7.9) 79 (15.8) 65 (29.4) Amniotic
Fluid Volume 26 (0.9) 11 (2.1) 11 (5.0) Abnormality 24 (0.8) 7
(1.3) 4 (1.8) *GA at time of invasive procedure. **Higher
penetrance of ultrasound abnormalities in fetuses with abnormal
karyotypes Abbreviations: BMI--Body Mass Index, IUGR--Intrauterine
growth retardation
Per the random sampling plan, all eligible subjects with an
abnormal karyotype were selected for analysis (FIG. 50B) as well as
a set of subjects carrying euploid fetuses so that the total
sequenced study population resulted in an approximately 4:1 ratio
of unaffected to affected subjects for trisomies 21. From this
process, 534 subjects were selected. Two samples were subsequently
removed from analysis due to sample tracking issues in which a full
chain of custody between sample tube and data acquisition did not
pass quality audit (FIG. 50). This resulted in 532 subjects for
analysis contributed by 53 of the 60 study sites. The demographics
of the selected cohort were similar to the overall cohort.
Test Performance
FIGS. 51A-51C show the flow diagram for aneuploidy analysis of
chromosomes 21, 18 and 13 and FIGS. 51D-51F show gender analysis
flow. Table 27 shows the sensitivity, specificity and confidence
interval for each of the six analyses, and FIGS. 52, 53, and 54,
show the graphical distribution of samples according to the NCVs
following sequencing. In all 6 categories of analysis, 16 samples
(3.0%) were removed due to no fetal DNA detected. After unblinding,
there were no distinguishing clinical features for these samples.
The number of censored karyotypes for each category was dependent
on the condition being analyzed (fully detailed in FIG. 52).
Sensitivity and specificity of the method to detect T21 in the
analysis population (n=493) were 100% (95% CI=95.9, 100.0) and 100%
(95% CI=99.1, 100.0), respectively (Table 27 and FIG. 51A). This
included correct classification for one complex T21 karyotype, 47,
XX, inv(7)(p22q32), +21, and two translocation T21 arising from
Robertsonian translocations one of which was also mosaic for
monosomy X (45, X, +21,der(14; 21)q10;q10)[4]/46, XY, +21,der(14;
21)q10;q10)[17] and 46, XY, +21,der(21; 21)q10; q10).
Sensitivity and specificity to detect T18 in the analysis
population (n=496) were 97.2% (85.5, 99.9) and 100% (99.2, 100.0)
(Table 27 and FIG. 51B). Although censored (as per protocol) from
the primary analysis, four samples with mosaic karyotype for T21
and T18 were all correctly classified by the method of the
invention as `affected` for aneuploidy (Table 25). Because they
were correctly detected they are indicated on the left side of
FIGS. 51A and 51B. All remaining censored samples were correctly
classified as unaffected for trisomies 21, 18, and 13 (Table 25).
Sensitivity and specificity to detect T13 in the analysis
population were 78.6% (49.2, 99.9) and 100% (99.2, 100.0) (FIG.
51C). One T13 case detected arose from a Robertsonian translocation
(46, XY, +13,der(13; 13).sub.q10;q10). There were seven
unclassified samples in the chromosome 21 analysis (1.4%), five in
the chromosome 18 analysis (1.0%), and two in the chromosome 13
analysis (0.4%) (FIG. 51A-51C). In all categories there was an
overlap of three samples that had both a censored karyotype
(69,XXX) and no fetal DNA detected. One unclassified sample in the
chromosome 21 analysis was correctly identified as T13 in the
chromosome 13 analysis and one unclassified sample in the
chromosome 18 analysis was correctly identified as T21 in the
chromosome 21 analysis.
TABLE-US-00025 TABLE 25 Censored Karyotypes Sequencing Sequencing
Censored Classification Classification Karyotype Category
Aneuploidy Gender Mosaic Trisomy 21 and 18 (n = 4) 47, XY,
+21[5]/46, XY[12] 21 Affected (T21) Male 47, XX, +21[4]/46, XX [5]
21 Affected (T21) Unclassified 47, XY, +21[21]/48, XY, +21 +
mar[4]* 21, 18, 13, Affected (T21) Male gender 47, XX, +18 [42]/46,
XX [8] 18 Affected (T18) Female Other Complex Mosaicism (n = 2) 45,
XY, -13[5]/46, XY, r(13) 13 Unaffected (21, 18, Male (p11.1q22)[15]
13) 92, XXXX[20]/46, XX[61] 21, 18, 13, Unaffected (21, 18,
Unclassified gender 13) Added material of uncertain origin (n = 5)
46, XX, add (X)(p22.1) 21, 18, 13, Unaffected (21, 18, Female
gender 13) 46, XY, add(10)(q26) 21, 18, 13, Unaffected (21, 18,
Male gender 13) 46, XY, add(15)(p11.2) 21, 18, 13, Unaffected (21,
18, Male gender 13) 47, XY, +mar/46, XY 21, 18, 13, Unaffected (21,
18, Male gender 13) 47, XX + mar [12]/46, XX[8] 21, 18, 13,
Unaffected (21, 18, Female gender 13) Triploidy (n = 10) 69, XXY
21, 18, 13, Unaffected (21, 18, Unclassified sex gender 13) 69, XXX
(n = 9) 21, 18, 13, Unaffected (21, 18, Female (n = 5) gender 13)
(n = 6) Unclassified Unclassified (n = 3) (n = 4) Sex Chromosome
Aneuploidy (n = 10) 47, XXX (n = 4) gender Unaffected (21, 18, XXX
(n = 3) 13) (n = 4) Monosomy X (n = 1) 47, XXY (n = 3) gender
Unaffected (21, 18, XXY (n = 2) 13) (n = 2) Unclassified
Unclassified (18)** (n = 1)** and Unaffected (21, 13) (n = 1) 47,
XYY (n = 3) gender Unaffected (21, 18, XYY (n = 3) 13) (n = 3)
Mosaic Monosomy X (n = 7) 45, X/46, XX (n = 3) gender Unaffected
(21, 18, Female (n = 2) 13) (n = 3) Monosomy X (n = 1) 45, X/47,
XXX gender Unaffected (21, 18, Monosomy X 13) 45, X/46, XY (n = 2)
gender Unaffected (21, 18, Male (n = 2) 13) (n = 2) 45, X, +21,
der(14; 21)(q10; q10)[4]/46, XY, gender Affected (T21) and Male
+21, der(14; 21)(q10; q10)[17] Unaffected (18, 13) Other Reasons (n
= 3) Gender not disclosed in report (n = 2) gender Unaffected (21,
18, Female (n = 2) 13) 46, XY with maternal cell contamination
gender Unaffected (21, 18, Male (n = 1) 13) *Subject excluded from
all analysis categories due to marker chromosome in one cell line.
**Subject with karyotype 48, XXY, +18 was unclassified in
chromosome 18 analysis and sex aneuploidy was not detected.
TABLE-US-00026 TABLE 26 Abnormal and complex karyotypes that were
not censored Sequencing Sequencing Classification Classification
Karyotype Aneuploidy Gender Monosomy X (n = 20) 45, X (n = 15)
Unaffected (21, 18, 13) Monosomy X 45, X (n = 4) Unaffected (21,
18, 13) Unclassified 45, X (n = 1) Unaffected (21, 18, 13) Female
Other Autosomal Trisomy or Partial Trisomy (n = 5) 47, XX, +16
Chromosome 16 Unclassified aneuploidy 47, XX, +20 Chromosome 20
Unclassified aneuploidy Partial trisomy Unaffected (21, 18, Female
6q12q16.3 and 6q16.3, 13)* no gender 47, XY, +22 Unaffected (21,
18, 13) Male 47, XX, +22 Unclassified (21, 18, Unclassified 13)
Translocations (n = 7) Balanced (n = 6) Unaffected (21, 18, 13)
correct class (Male or Female) Unbalanced (n = 1) Unaffected (21,
18, 13) Female Other Complex Unaffected (21, 18, 13) correct class
(Male or Mosaicism (n = 4) Female) Other Complex Unaffected (21,
18, 13) correct class (Male or Variants (n = 4) Female) *An
increased normalized chromosome value (NCV) of 3.6 was noticed from
sequencing tags in chromosome 6 after unblinding.
The sex chromosome analysis population for determining performance
of the method (female, male, or monosomy X) was 433. Our refined
algorithm for classifying the gender status, which allowed for
accurate determination of sex chromosome aneuploidies, resulted in
a higher number of unclassified results. Sensitivity and
specificity for detecting diploid female state (XX) were 99.6% (95%
CI=97.6, >99.9) and 99.5% (95% CI=97.2, >99.9), respectively;
sensitivity and specificity to detect male (XY) were both 100% (95%
CI=98.0, 100.0); and sensitivity and specificity for detecting
monosomy X (45,X) were 93.8% (95% CI=69.8, 99.8) and 99.8% (95%
CI=98.7, >99.9) (FIGS. 33D-f). Although censored from the
analysis (as per protocol), the sequencing classifications of
mosaic monosomy X karyotypes were as follows (Table 25): 2/7
classified as monosomy X, 3/7 classified with a Y chromosome
component classified as XY and 2/7 with XX chromosome component
classified as female. Two samples that were classified according to
the method of the invention as monosomy X had karyotypes of 47,XXX
and 46, XX. Eight of ten sex chromosome aneuploidies for karyotypes
47,XXX, 47,XXY and 47,XYY were correctly classified (Table 25). If
the sex chromosome classifications had been limited to monosomy X,
XY and XX, most of the unclassified samples would have been
correctly classified as male, but the XXY and XYY sex aneuploidies
would not have been identified.
In addition to accurately classifying trisomies 21, 18, 13 and
gender, the sequencing results also correctly classified aneuploidy
for chromosomes 16 and 20 in two samples (47,XX, +16 and 47,XX,
+20) (Table 26). Interestingly, one sample with a clinically
complex alteration of the long arm of chromosome 6 (6q) and two
duplications, one of which was 37.5 Mb in size, showed an increased
NCV from sequencing tags in chromosome 6 (NCV=3.6). In another
sample, aneuploidy of chromosome 2 was detected according to the
method of the invention but not observed in the fetal karyotype at
amniocentesis (46,XX). Other complex karyotype variants shown in
Tables 25 and 26 include samples from fetuses with chromosome
inversions, deletions, translocations, triploidy and other
abnormalities that were not detected here, but could potentially be
classified at higher sequencing density and/or with further
algorithm optimization using the method of the invention. In these
cases, the method of the invention correctly classified the samples
as unaffected for trisomy 21, 18, or 13 and as male or female.
In this study, 38/532 analyzed samples were from women who
underwent assisted reproduction. Of these, 17/38 samples had
chromosomal abnormalities; no false positives or false negatives
were detected in this sub-population.
TABLE-US-00027 TABLE 27 Sensitivity and Specificity of the Method
Sensitivity Specificity Performance (%) 95% CI (%) 95% CI Trisomy
21 100.0 95.9-100.0 100.0 99.1-100.0 (n = 493) (89/89) (404/404)
Trisomy 18 97.2 85.5-99.9 100 99.2-100.0 (n = 496) (35/36)
(460/460) Trisomy 13 78.6 49.2-99.9 100.0 99.2-100.0 (n = 499)
(11/14) (485/485) Female 99.6 97.6->99.9 99.5 97.2->99.9 (n =
433) (232/233) (199/200) Male 100.0 98.0-100.0 100.0 98.5-100.0 (n
= 433) (184/184) (249/249) Monosomy X 93.8 69.8-99.8 99.8
98.7->99.9 (n = 433) (15/16) (416/417)
Discussion
This prospective study to determine whole chromosome fetal
aneuploidy from maternal plasma was designed to emulate the real
world scenario of sample collection, processing and analysis. Whole
blood samples were obtained at the enrollment sites, did not
require immediate processing, and were shipped overnight to the
sequencing laboratory. In contrast to a prior prospective study
that only involved chromosome 21 (Palomaki et al., Genetics in
Medicine 2011:1), in this study, all eligible samples with any
abnormal karyotype were sequenced and analyzed. The sequencing
laboratory did not have prior knowledge of which fetal chromosomes
might be affected nor the ratio of aneuploid to euploid samples.
The study design recruited a high-risk study population of pregnant
women to assure a statistically significant prevalence of
aneuploidy, and Tables 25 and 26 indicate the complexity of the
karyotypes that were analyzed. The results demonstrate that: i)
fetal aneuploidies (including those resulting from translocation
trisomy, mosaicism, and complex variations) can be detected with
high sensitivity and specificity and ii) aneuploidy in one
chromosome does not affect the ability of the method of the
invention to correctly identify the euploid status of other
chromosomes. The algorithms utilized in the previous studies appear
to be unable to effectively determine other aneuploidies that
inevitably would be present in a general clinical population (Erich
et al., Am J Obstet Gynecol 2011 March; 204(3):205 e1-11, Chiu et
al., BMJ 2011; 342:c7401).
With regard to mosaicism, the analysis of sequencing information in
this study was able to correctly classify samples that had mosaic
karyotypes for chromosomes 21 and 18 in 4/4 affected samples. These
results demonstrate the sensitivity of the analysis for detecting
specific characteristics of cell free DNA in a complex mixture. In
one case, the sequencing data for chromosome 2 indicated a whole or
partial chromosome aneuploidy while the amniocentesis karyotype
result for chromosome 2 was diploid. In two other examples, one
sample with 47,XXX karyotype and another with a 46,XX karyotype,
the method of the invention classified these samples as monosomy X.
It is possible these are mosaic cases, or that the pregnant woman
herself is mosaic. (It is important to remember that the sequencing
is performed on total DNA, which is a combination of maternal and
fetal DNA.) While cytogenetic analysis of amniocytes or villi from
invasive procedures is currently the reference standard for
aneuploidy classification, a karyotype performed on a limited
number of cells cannot rule out low-level mosaicism. The current
clinical study design did not include long term infant follow-up or
access to placental tissue at delivery, so we are unable to
determine if these were true or false positive results. We
speculate that the specificity of the sequencing process, coupled
with optimized algorithms according to the method of the invention
to detect genome wide variation, may ultimately provide more
sensitive identification of fetal DNA abnormalities, particularly
in cases of mosaicism, than standard karyotyping.
The International Society for Prenatal Diagnosis has issued a Rapid
Response Statement commenting on the commercial availability of
massively parallel sequencing (MPS) for prenatal detection of Down
syndrome (Benn et al., Prenat Diagn 2012 doi:10.1002/pd.2919). They
state that before routine MPS-based population screening for fetal
Down syndrome is introduced, evidence is needed that the test
performs in some sub-populations, such as in women who conceive by
in vitro fertilization. The results reported here suggest that the
present method is accurate in this group of pregnant women, many of
whom are at high risk for aneuploidy.
Although these results demonstrate the excellent performance of the
present method with optimized algorithms for aneuploidy detection
across the genome in singleton pregnancies from women at increased
risk for aneuploidy, more experience, particularly in low-risk
populations, is needed to build confidence in the diagnostic
performance of the method when the prevalence is low and in
multiple gestation. In the early stages of clinical implementation,
classification of chromosomes 21, 18 and 13 using sequencing
information according to the present method should be utilized
after a positive first or second trimester screening result. This
will reduce unnecessary invasive procedures caused by the false
positive screening results, with a concomitant reduction in
procedure related adverse events. Invasive procedures could be
limited to confirmation of a positive result from sequencing.
However, that there are clinical scenarios (e.g., advanced maternal
age and infertility) in which pregnant women will want to avoid an
invasive procedure; they may request this test as an alternative to
the primary screen and/or invasive procedure. All patients should
receive thorough pre-test counseling to ensure that they understand
the limitations of the test and the implications of the results. As
experience accumulates with more samples, it is possible that this
test will replace current screening protocols and become a primary
screening and ultimately a noninvasive diagnostic test for fetal
aneuploidy.
Example 17
Determining Fetal Fraction from NCV to Distinguish the Presence of
Complete or Partial Fetal Chromosomal Aneuploidies in Analytical
Samples
Given that the chromosome dose for a fetal chromosome of interest
in a maternal sample increases proportionately with increasing
fetal fraction, it is expected that a ff value that is based on the
NCV value for a complete chromosome of interest would be
determinative of the presence or absence of a complete fetal
chromosomal aneuploidy. To demonstrate that ff determined from NCVs
can be used to distinguish the presence of a complete chromosomal
aneuploidy from a partial chromosomal aneuploidy or the
contribution from a mosaic sample, genomic DNA from mothers and
from their children were used to create artificial samples that
simulated the mixture of fetal and maternal cfDNA found in the
circulation of a pregnant woman. The NCV based value of fetal
fraction is a form of putative fetal fraction described above.
The DNA of the mothers and children was purchased from Coriell
Institute for Medical Research (Camden, N.J.). DNA identification
and sample karyotype are given in Table 27.
TABLE-US-00028 TABLE 27 Example 17 Artificial Clinical mixture #
Condition Coriell ID Family Member Comments Pres Karyotype 1 Whole
trisomy; NG09387 2139 Mother Normal Normal 46, XX NG09394 2139 Son
Affected Downs 47, XY, +21 T21 Syndrome 2 Deletion NA10924 1313
Mother Normal Normal 46, XX NA10925 1313 Son Deletion in Grieg 46,
XY, del(7) 7 Encephaly (pter>p14:: p12>qter) 3 Mosaic NA22629
2877 Mother Deletion in Affected 46, XX, del(11) 11 NA22628 2877
Son Deletion in Affected 47, XY, del(11) 11, Mos mosaic (pter->
dup 15 p12::p11.2-> qter), +15[12]/ 46, XY, del(11) (pter->
p12::p11.2-> qter)[40].arr 11p12p11.12(41 392049- 49104319)x1 4
Duplication NA16368 1925 Mother Normal 46, XX.arr(1- 22, X)x2
NA16363 1925 Nor Twin Monozygotic Normal 46, XY Son Twins, one
normal, one affected NA16362 1925 Affected Affected 47, XY, twin
son; +der(22) partial T22
Samples comprising complete chromosomal or partial chromosomal
aneuploidies were analyzed as follows.
In all cases, genomic DNA from the mother and genomic DNA from the
child were sheared by sonication with a peak at 200 bp. Artificial
samples comprising mothers' DNA with 0%, 5% or 10% w/w of the
child's DNA spiked in were processed to prepare sequencing
libraries, which were sequenced in a massively parallel fashion
using sequencing-by-synthesis as described in Example 12. Each
artificial DNA sample was sequenced four times using separate flow
cells on the sequencer to provide 4 sets of sequence information
for each of samples containing 0%, 5% and 10% child DNA. 36 bp
reads were aligned to human reference genome hg19, and uniquely
mapped tags were counted. Approximately 125.times.10.sup.6 sequence
tags were obtained for each of the 4 flow cell lanes used per
sample.
Normalizing chromosomes (single or group of chromosomes) were
identified in a set of qualified samples comprising 20 male and 20
female gDNA libraries, as described elsewhere herein. Normalizing
chromosomes for chromosome 21 were identified chr4+chr16+chr22
normalizing chromosomes for chromosome 7 were identified as
chr4+chr6+chr8+chr12+chr19+chr20normalizing chromosomes for
chromosome 15 were identified as chr9+chr12+chr14+chr19+chr20,
normalizing chromosome for chromosome 22 were identified as chr19
and normalizing chromosomes for chromosome X were identified as
chr4+chr6+chr7+chr8. Sequence tags for the chromosome of interest
and for the corresponding normalizing chromosome (single chromosome
or group of chromosomes) obtained from sequencing the artificial
samples were counted and used to calculate chromosome doses, and
calculate NCVs.
In the instant example, the ff determined using NCV for chromosome
21 in a sample mixture (1) where NCV.sub.21A is the NCV value
determined for chromosome 21 in the test sample (1), which
comprises the triploid chromosome 21, and CV.sub.21U is the
coefficient of variation for doses of chromosome 21 determined in
the qualified samples (comprising diploid chromosome 21); and where
NCV.sub.XA is the NCV value determined for chromosome X in the test
sample (1), which comprises the triploid chromosome 21, and
CV.sub.XU is the coefficient of variation for doses of chromosome X
determined in the qualified samples (comprising diploid chromosome
21).
FIG. 56 shows a plot of the percent "ff" determined using doses of
chromosome 21 (ff.sub.21) as a function of the percent "ff"
determined using doses of chromosome X (ff.sub.X) in a synthetic
maternal sample (1) comprising DNA from a child with trisomy
21.
The data shows that the chromosome doses and the NCVs derived
therefrom increase in proportion with increasing ff, and that there
is a 1:1 relationship between the percent ff determined using doses
for the triploid chromosome i.e. chromosome 21, and the percent ff
determined using doses for a chromosome known to be present as a
single chromosome i.e. chromosome X.
FIG. 57 shows a plot of the percent "ff" determined using doses of
chromosome 7 (ff.sub.7) as a function of the percent "ff"
determined using doses of chromosome X (ff.sub.X) in a synthetic
maternal sample (2) comprising DNA from a euploid mother and her
child who carries a partial deletion in chromosome 7.
As was shown for samples (1) and (2), the data show that the
chromosome doses and the NCVs derived therefrom increase in
proportion with increasing ff. However, in a case where the
aneuploidy is a partial chromosomal aneuploidy, the percent ff
determined using chromosome doses of a partially aneuploid
chromosome (ff.sub.7) does not correspond to the percent ff
determined using doses for chromosome X (ff.sub.X). Therefore,
deviation from the 1:1 relationship shown for a complete trisomic
sample is indicative of the presence of a partial aneuploidy.
FIG. 58 shows a plot of the percent "ff" determined using doses of
chromosome 15 (ff.sub.15) as a function of the percent "ff"
determined using doses of chromosome X (ff.sub.X) in a synthetic
maternal sample (3) comprising DNA from a euploid mother and her
child who is 25% mosaic with a partial duplication of chromosome
15.
As was shown for samples (1) and (2), the ff determined using doses
and the NCVs derived therefrom increase in proportion with
increasing ff. As was shown in sample (2), sample (3) comprises a
partial chromosomal aneuploidy, and the percent ff determined using
chromosome doses of a partially aneuploid chromosome (ff.sub.15)
does not correspond to the percent ff determined using doses for
chromosome X (ff.sub.X). The lack of correspondence between the two
ff is indicative of the presence of a partial aneuploidy rather
than a complete chromosomal aneuploidy.
FIG. 59 shows a plot of the percent "ff" determined using doses of
chromosome 22 (ff.sub.22) and the NCVs derived therefrom in
artificial sample (4) comprising 0% child DNA (i), and 10% DNA from
an unaffected twin son known not to have a partial chromosomal
aneuploidy of chromosome 22 (ii), and 10% DNA from the affected
twin son known to have a partial chromosomal aneuploidy of
chromosome 22 (iii). The data show that the "ff" for the sample
comprising the DNA from the unaffected twin and determined from the
four NCVs calculated from doses of chromosome 22 are close to zero,
indicating the absence of an aneuploidy of chromosome 22 in the
unaffected child; and the "ff" of the unaffected twin when
calculated from doses of chromosome X confirm that the "ff" for the
unaffected twin sample is about 10%. The data also show that the
"ff" for the sample comprising DNA from the affected twin and
determined from the four NCVs calculated from doses of chromosome
22 (ff.sub.22) is about 3%, indicating the presence of an
aneuploidy in chromosome 22; while the "ff" when calculated from
doses of chromosome X (ff.sub.X) confirm that the "ff" for the
unaffected twin sample is about 10%. The lack of correspondence
between the ff.sub.22 and ff.sub.X indicates that the aneuploidy of
chromosome 22 in the affected twin is a partial chromosomal
aneuploidy.
Therefore, the data shows that in maternal samples comprising cfDNA
of a male fetus, the chromosome doses and the NCV values derived
therefrom can be used to distinguish the presence of a complete
trisomy from a partial aneuploidy and/or a complete or partial
aneuploidy present in a mosaic sample. The partial aneuploidy can
be an increase or a decrease of part of a chromosome. Optionally,
resolution of the partial aneuploidy and/or mosaicism can be
obtained by using chromosome doses and Estimated Fetal Fraction
values as described in Example 12.
The fetal fraction methods described above can also be utilized to
determine the likelihood that one or more of the fetus' in
multi-gestational pregnancy has an aneuploidy. For example, in one
case of fraternal twins the fetal fraction determined from the
NCV.sub.X value was found to be 8.3% while that measured from the
NCV.sub.21 value was 5.0%. This suggested that only one of the pair
of male fetuses had a T21 aneuploidy, and this result is confirmed
by the karyotype result. In another example with maternal twins the
fetal fraction determined from the X chromosome was 7.3% whereas
fetal fraction determined from chromosome 18 was 8.9%. In this
example, both twins were determined to be T18 males from
karyotype.
Example 18
Determining Fetal Fraction from NCV to Identify the Presence of
Complete Fetal Chromosomal Aneuploidies in Clinical Samples
To demonstrate that a ff determined from NCVs (CNff) can be used to
distinguish the presence of a complete chromosomal aneuploidy from
a partial chromosomal aneuploidy in a clinical sample, chromosomes
of interest 21, 13, and 18 were quantified in clinical samples
using cfDNA obtained from the blood of pregnant women. The presence
of trisomy was verified by karyotpe.
cfDNA was obtained from 46 maternal samples from pregnant women
each carrying a male fetus with trisomy 21 (T21), 13 maternal
samples from pregnant women each carrying a fetus with trisomy 18
(T18), and 3 maternal samples from pregnant women carrying a male
fetus with trisomy 13 (T13). These clinical samples were samples
from the clinical study described in Example 16. cfDNA was
isolated, and sequencing libraries were prepared as described in
Example 16, but using the new Illumina v3 chemistry.
Sequencing libraries made from cfDNA from qualified samples known
to be unaffected for chromosomes 21, 18 and 13 were also sequenced
using the Illumin v3 chemistry. Sequence reads obtained for the
qualified samples were mapped to human reference genome hg19 and
Sequence reads that uniquely mapped all chromosome sequences
corresponding to human reference genome hg19 (non-repeat masked)
were counted and used to systematically determine which chromosome
or group of chromosomes would serve as the normalizing chromosome
for each of chromosomes of interest 21, 18, and 13 in the test
samples.
Table 28 below shows the normalizing chromosomes (denominator
chromosomes) identified to be used to determine chromosome doses
(ratios) for chromosomes 1-22, X and Y in each of the test
samples.
TABLE-US-00029 TABLE 28 Example 18 - Normalizing chromosomes
systematically identified for use in T21, T18, and T13 test samples
chromosome % cv_1 mean_1 stdv_1 denominator_1 chr1 0.17328043
0.40761174 0.00070631 chr2 + chr10 + chr15 + chr20 + chr22 chr2
0.12704695 0.28019322 0.00035598 chr1 + chr4 + chr6 + chr8 + chr10
chr3 0.15988408 0.40355832 0.00064523 chr5 + chr6 + chr8 chr4
1.74801104 1.01640701 0.01776691 chr5 chr5 0.12567875 0.26828505
0.00033718 chr3 + chr4 + chr8 + chr12 chr6 0.18609738 0.23679013
0.00044066 chr2 + chr3 + chr4 + chr14 chr7 0.15420267 0.14975583
0.00023093 chr4 + chr5 + chr6 + chr8 + chr9 + chr12 + chr19 + chr22
chr8 0.16386037 0.14886515 0.00024393 chr3 + chr4 + chr5 + chr11 +
chr12 + chr14 + chr20 chr9 0.14260705 0.07866201 0.00011218 chr1 +
chr2 + chr5 + chr7 + chr8 + chr11 + chr14 + chr15 + chr16 + chr17 +
chr22 chr10 0.23668533 0.27352768 0.0006474 chr1 + chr6 + chr20 +
chr22 chr11 0.15337497 0.18482929 0.00028348 chr1 + chr5 + chr8 +
chr16 + chr20 + chr22 chr12 0.15469865 0.16993862 0.00026289 chr3 +
chr5 + chr6 + chr14 + chr17 + chr20 chr13 0.43818368 0.26647091
0.00116763 chr4 + chr6 chr14 0.21119571 0.25952538 0.00054811 chr5
+ chr12 + chr22 chr15 0.43655328 0.19120781 0.00083472 chr1 + chr10
+ chr20 chr16 0.40796729 0.2909714 0.00118707 chr15 + chr17 + chr19
+ chr20 chr17 0.43044876 0.42765351 0.00184083 chr16 + chr20 +
chr22 chr18 0.2411015 0.23996728 0.00057856 chr5 + chr8 chr19
1.31524683 1.42233899 0.01870727 chr22 chr20 0.32975718 0.17240557
0.00056852 chr10 + chr16 + chr17 + chr19 + chr22 chr21 0.43611264
0.08516148 0.0003714 chr4 + chr14 + chr16 + chr17 chr22 1.31897082
0.70318839 0.00927485 chr19 chrx 0.67161441 0.28361966 0.00190483
chr4 + chr5 + chr8 chry 12.85179682 0.00035758 0.00004596 chr4 +
chr7
Having identified the normalizing chromosomes in the qualified
samples, the test samples were sequenced, and sequence tags mapping
to each of chromosomes 21, 18, 13, and corresponding normalizing
chromosomes in the test samples were counted and used to calculate
chromosome doses (ratios). NCV values were then calculated as
described previously according to
.sigma..times..times. ##EQU00031## For each of the test samples,
the fetal fraction was determined for chromosome x and for the
chromosome of interest according to the equation
ff.sub.(i)=2*NCV.sub.jACV.sub.jU Equation 25 described elsewhere in
the specification.
FIG. 60 shows a plot of the CNffx versus CNff21 determined in the
samples comprising the fetal T21 trisomy. As expected for a
complete chromosomal aneuploidy, the CNffx matched that determined
using NCVs from chromosome 21 (CNff21).
Similarly, CNffx matched that determined using NCVs from chromosome
18 (CNff18) in the T18 test samples (FIG. 61), and CNffx matched
that determined using NCVs from chromosome13 (CNff13) in the T13
test samples (FIG. 62).
FIG. 60 also shows the fetal fraction obtained for the samples with
female fetuses affected by T21. As expected, CNff21 in these
"female" samples could not be verified by comparison to chromosome
X. In order to verify the CNff21 for the female samples, CNff can
be determined for a chromosome known not to the aneuploid in a
fetus e.g. chromosome 1. Alternatively, CNff21 for "female" samples
can be confirmed by comparing it to a NCNff e.g. one determined by
counting tags to polymorphic sequences, as described elsewhere
herein.
Therefore, the number of sequence tags and the derived NCV values
that identify copy number variations of complete chromosomes can be
used to determine the corresponding fetal fraction in the
aneuploid/affected samples. Correspondence in the CNff for a
chromosome of interest with that of a chromosome known not to be
aneuploid can be used to confirm the presence of a complete
chromosomal trisomy.
Example 19
Determining Fetal Fraction from NCV to Identify the Presence of
Partial Fetal Chromosomal Aneuploidies in Clinical Samples
To demonstrate that a ff determined from NCVs (CNff) can be used to
identify and localize the presence of a partial chromosomal
aneuploidy from a partial chromosomal aneuploidy in a clinical
sample, cfDNA from a clinical that had been identified as having an
aneuploidy in chromosome 17, was sequenced and analyzed as
described in Example 18.
Using sequence tags mapped to chromosome 17 in the test sample, and
to normalizing chromosomes chr16+chr20+chr22 that been identified
in the set of qualified samples (Table 28 above), NCV values for
each of chromosomes in the test sample were calculated.
FIG. 63 shows a plot of NCV values for chromosomes 1-22 and X in
the test sample. As is shown in the plot, the NCV value for
chromosome 17 was determined to have an NCV>4, which is the
threshold that had been chosen for identifying aneuploid
chromosomes. The plot also shows the NCV value for chromosome X,
which as expected had a negative NCV.
The CNff for chromosome 17 and chromosome X were calculated
according to ff.sub.(i)=2*NCV.sub.jACV.sub.jU Equation 25 and
determined to be CNff17=3.9% and CNffX=13.5%.
The discrepancy between the CNff indicated the presence of either a
partial aneuploidy or possibly of a mosaicism.
To distinguish the partial aneuploidy from a possible mosaicism,
the number of tags counted for each of 100 Kbp consecutive
blocks/bins on chromosome 17, and a normalized bin value (NBV) was
calculated for each bin. Normalization of the number of tags in
individual bins was performed by determining the ratio of tags/bin
to the sum of the number of tags in 20 bins of identical size and
having a GC content closest to that of the bin being analyzed.
Thus, in this instance, normalization was related to GC content.
Optionally, bin normalization can also be related to the
variability in bin dose as determined in qualified samples as
described for chromosome doses/ratios. In this example, the GCC
Z-score is equivalent to the NBV value determined as
.times..times. ##EQU00032## where M.sub.j and MAD.sub.j are the
estimated median and median adjusted deviation, respectively, for
the j-th chromosome dose in a set of qualified samples, and
x.sub.ij is the observed j-th chromosome dose for test sample
i.
The normalized bin values (NBV) for each of the 100 Kbp bins along
the length of chromosome 17 are shown on the Y-axis of FIG. 64 as
GCC Z-score, indicating the GC normalization. The plot shown in
FIG. 64 clearly shows an increase in copy number of the bins
corresponding to approximately the last 200,000 bp of chromosome
17. This finding was in agreement with the karyotype provided for
the sample indicating a duplication at the q ter of chromosome
17.
Therefore, CNff can be used to identify and to localize partial
aneuploidies in chromosomes.
Example 20
Noninvasive Detection of Fetal Sub-Chromosome Abnormalities Using
Deep Sequencing of Maternal Plasma
Artificial Mixtures
Artificial mixtures of 5% and 10% sheared genomic DNA were prepared
using paired mother and child DNAs obtained from the Coriell
Institute for Medical Research (Camden, N.J.). All children were
males with karyotypes previously determined by metaphase
cytogenetic analysis. The karyotypes of the four paired samples are
shown in Table 29. The children's chromosome abnormalities were
selected to represent different clinical scenarios, such as: a)
whole chromosome aneuploidy (family 2139), b) sub-chromosomal
deletion (family 1313), c) mosaic sub-chromosomal copy number
change (family 2877, with an additional inherited deletion), and d)
sub-chromosomal duplication (family 1925).
The genomic DNA samples were sheared to a size of .about.200 bp
using the Covaris S2 sonicator (Covaris, Woburn, Mass.) following
the manufacturer's recommended protocols. DNA fragments smaller
than 100 bp were removed using AmPure XP beads (Beckman Coulter
Genomics, Danvers, Mass.). Sequencing libraries were generated with
TruSeq v1 Sample Preparation kits (Illumina, San Diego, Calif.)
from sheared DNA mixtures consisting of maternal DNA only and
maternal+child DNA mixtures at 5% and 10% w/w. Samples were
sequenced with single-ended 36 base pair (bp) reads on the Illumina
HiSeq2000 instrument using TruSeq v3 chemistry. Each sample was
sequenced on four lanes of a flow cell, resulting in
400.times.10.sup.6 to 750.times.10.sup.6 sequence tags per
sample.
Maternal Plasma Samples
The MatErnal BLood IS Source to Accurately Diagnose Fetal
Aneuploidy (MELISSA) trial was a registered clinical trial
(NCT01122524) that recruited subjects and samples from 60 different
centers in the United States. The study was designed to
prospectively determine the accuracy of MPS (massively parallel
sequencing) to detect whole chromosome fetal aneuploidy. During
this trial, all samples with any abnormal karyotype were included
to emulate the real clinical scenarios in which the fetal karyotype
is not known at the time of sample acquisition. The results of this
study have been previously published. Following completion of the
MELISSA trial, the study database was assessed to identify ten
samples that had complex karyotypes, including sub-chromosome
abnormalities, material of unknown origin, or a marker chromosome
(Table 30); also added was one MELISSA study sample with trisomy 20
as a control of performance in detection of whole chromosome
aneuploidy. The karyotypes were performed for clinical indications
and reflected local protocols. For example, some samples were
analyzed with chromosome microarrays and some had metaphase
analysis with or without FISH studies.
In the MELISSA study libraries were sequenced using single-end
reads of 36 bp with 6 samples in a lane on an Illumina HiSeq2000
using TruSeq v2.5 chemistry. In the present example, the previously
generated MELISSA libraries were re-sequenced using TruSeq v3
chemistry on an Illumina HiSeq 2000 with single-end reads of 25 bp.
In this example, each of the 11 maternal samples was sequenced
utilizing an entire flow cell, resulting in 600.times.10.sup.6 to
1.3.times.10.sup.9 sequence tags per sample. All sequencing was
performed in the Verinata Health research laboratory (Redwood City,
Calif.) by research laboratory personnel who were blinded to the
fetal karyotype.
TABLE-US-00030 TABLE 29 Coriell samples used to generate artificial
mixtures. amily ID oriell ID Member Karyotype 139 G09387 Mother 46,
XX G09394 Affected 47, XY, +21 Son 313 A10924 Mother 46, XX A10925
Affected 46, XY, del(7)(pter > p14::p12 > qter) Son 877
A22629 Mother 46, XX, del(11) A22628 Affected 47, XY,
del(11)(pter-> son p12::p11.2 > qter), +15[12]/ 46, XY,
del(11)(pter->p12::p11.2->qter)[40] 925 A16268 Mother 46, XX
A16363 Unaffected 46, XY twin son A16362 Affected 47, XY, +der(22)
twin son
TABLE-US-00031 TABLE 30 Karyotypes of clinical samples analyzed by
MPS. Samples with shading are mosaic karyotypes. ##STR00001##
Normalization and Analysis
Sequence reads were aligned to the human genome assembly hg19
obtained from the UCSC database
(hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/). Alignments were
carried out utilizing the Bowtie short read aligner (version
0.12.5), allowing for up to two base mismatches during alignment.
Only reads that unambiguously mapped to a single genomic location
were included. Genomic sites at which reads mapped were counted as
tags. Regions on the Y chromosome at which sequence tags from male
and female samples mapped without any discrimination were excluded
from the analysis (specifically, from base 0 to base
2.times.10.sup.6; base 10.times.10.sup.6 to base 13.times.10.sup.6;
and base 23.times.10.sup.6 to the end of chromosome Y).
The genome was then further divided into 1 Mb and 100 kb bins and,
for each sample, tags from both the positive and negative strand
were assigned to individual bins for further analysis. The GC
percentage of each bin was determined and bins were ranked by GC
percentage across the entire genome. Each bin was individually
normalized by calculating the ratio of tags within a bin to the sum
of the number of tags in the 10 bins with the nearest GC
percentages by equation (27):
.times. ##EQU00033##
Where BRV.sub.ij is the "Bin Ratio Value" for the j.sup.th bin of
chromosome i, and Tags.sub.ij is the number of tags in the j.sup.th
bin of chromosome i. The sum runs over the 10 bins for the 1 Mb
data and 40 bins for the 100 kb data for bins (kin) with the
nearest GC percentage to bin ij.
In order to detect any sub-chromosomal differences, each of the
BRVs were examined for deviations from the median values measured
across multiple samples. The medians were determined from the four
maternal only DNAs (Table 29) for the artificial samples and from
the eleven maternal plasma samples (Table 30) for the clinical
samples. Median absolute deviations (MADs) were calculated for each
bin based on the medians and adjusted assuming a normal
distribution for the number of tags in each bin. The adjusted MADs
(aMADs) were utilized to calculate a z-score for each bin by
equation (28):
##EQU00034##
It was expected that z.sub.ij would be approximately .+-.3 for
regions without any copy number variations (CNVs) and significantly
greater than 3 when fetal CNVs were present.
The z.sub.ij values can be utilized to determine the relative fetal
fraction (ff) present in the cfDNA. The value can then be compared
to an independent measurement of ff to validate copy number
detection, or suggest the presence of mosaicism. For a bin ratio
containing a copy number change from normal, the BRV.sub.ij will
increase (in the case of a duplication) or decrease (in the case of
a deletion) by equation (29):
.+-..times. ##EQU00035##
In this equation, ff.sub.n is the fetal fraction for sample n. If
the coefficient of variation for each bin, CV.sub.ij is defined as
equation (30):
##EQU00036##
then equation (31) ff.sub.n=abs(2z.sub.ijCV.sub.ij)
can be used to calculate ff.sub.n for sample n from z.sub.ij
values.
Detection of a sub-chromosomal abnormality was a multi-step process
for classifying specific regions as having a copy number variant.
In step 1, z.sub.ij values from the 1 Mb bins that exceeded .+-.4
were identified. The calculated ff was then utilized and bins that
had a ff of less than 4% were eliminated. For the samples with male
fetuses, the ff was also calculated using all of the bins in
chromosome X. This value was compared to the result obtained for
putative copy number changes to validate a copy number change or
suggest a mosaic result. Finally, in cases of a single 1 Mb bin
that met the above criteria, the 100 kb bins data were examined and
it was required that at least 2 bins (within a contiguous group of
4) indicated a z.sub.ij value that exceeded +4 or -4 before
classifying a sample as having a copy number variant.
Results
Artificial Mixtures
Whole Chromosome Aneuploidy of Chromosome 21
FIG. 65 shows the chromosome 21 z.sub.21j values (1 Mb bins) for an
artificial mixture of family 2139 with 10% of the son's DNA (T21)
mixed with the mother's DNA. In chromosome 21, there are
approximately 38 Mb in the q arm that contain unique reference
genome sequence in hg19. All of the chromosome 21 tags mapped to
this region. With the exception of the first 4 Mb, FIG. 65 shows an
over-representation of most of chromosome 21 in the 10% mixture, as
would be expected with a full chromosome aneuploidy. Using equation
(5) to calculate the ff from the average z.sub.21j values of the
amplified regions, ffs of 7.0% and 12.7%, for the 5% and 10%
mixtures, respectively, were obtained. Calculating the ff average
using z.sub.Xj values, ffs of 4.2% and 9.0%, for the 5% and 10%
mixtures, respectively, were obtained.
Sub-Chromosomal Deletion of Chromosome 7
The method was next tested on Family 1313, in which the son has a
sub-chromosomal deletion of chromosome 7. FIG. 66 shows the
chromosome 7 z.sub.7j values (1 Mb bins) for the maternal sample
mixed with 10% of her son's DNA. A deletion was observed beginning
at bin 38 and continuing to bin 53. This reflects the approximately
15 Mb deletion documented in the metaphase karyotype. Fetal
fraction values ffs of 6.1% and 10.5% were calculated for the 5%
and 10% mixtures, respectively, for this sample. Calculating the ff
average using z.sub.Xj values, ffs of 5.9% and 10.4% were obtained,
respectively. Interestingly in this sample there appeared to be a
duplication in the maternal sample at bin 98 of chromosome 7
(circle in FIG. 66), which did not appear in the son, i.e. was not
inherited, and a concomitant value decrease was observed in the
mixture. Bin 2 which had very high z.sub.72 values of 43.9 and 28.5
for the maternal sample and 10% mixture, respectively (data not
shown) also appeared to reflect a maternal duplication.
Mosaic Duplication of Chromosome 15
In Family 2877, the maternal sample has a deletion in chromosome 11
that was inherited by the son. In addition, the son has a
duplication in chromosome 15 that was not maternally inherited, and
is part of a mosaic karyotype in which the majority of cells are
normal (Table 29). FIG. 67 shows both the chromosome 11 and
chromosome 15 z.sub.ij values for the 1 Mb bins in the mixture with
10% of the son's DNA. As expected, the inherited deletion in
chromosome 11 had a consistent set of values that did not change
with fetal fraction. However, the chromosome 15 duplication was
clearly detected between bins 28 and 65, albeit with more noise
than observed in the other artificial samples. The noise results
from the reduced apparent ff for this duplication due to the
mosaicism. The ffs calculated from the duplication were 1.6% and
3.0% for the 5% and 10% mixtures, respectively. In contrast, the
ffs calculated from chromosome X were 5.3% and 10.7%. The method
was able to detect both the sub-chromosomal duplication with the
low mosaic ff and to distinguish that the duplication was due to
mosaicism by comparison of the ff result to an independent
measurement of chromosome X.
Duplications of Chromosome 22
Family 1925 consisted of a mother and two male twins, one of which
had two duplications of different sizes in chromosome 22. Ten
percent mixtures of the affected twin's DNA and the mother were
sequenced. The results indicated a 2 Mb and an 8 Mb duplication at
bins 17 and 43, respectively. The ff for 10% mixture was calculated
to be 11.2% from the 2 Mb duplication, 11.6% from the 8 Mb
duplication, and 9.8% from chromosome X (data not shown).
Maternal Plasma Samples
Whole Chromosome Aneuploidy
Sample C60715 was previously reported in MELISSA study as detected
for trisomy 20. The ff calculated from the whole chromosome 20 data
(i.e. .about.11 million tags without binning) was 4.7%. The 1 Mb
bin results for this sample contain .about.960 million tags across
the genome. The extra copy of chromosome 20 was clearly detected
and the ff calculated from the 1 Mb bin data is 4.4%, in agreement
with the whole chromosome results.
Duplications and Deletions
Sample C65104 (Table 31) had a complex fetal karyotype that
involved the long arm of chromosome 6 (6q) and two duplications,
one of which was 37.5 Mb in size. The second duplication was
reported as approximately 650 kb from the chromosome microarray
analysis of cultured villi. Using MPS it was previously reported
that this sample showed an increased whole chromosome normalized
chromosome value (NCV) in chromosome 6 (NCV=3.6). Bianchi, D. W.,
Platt, L. D., Goldberg, J. D., Abuhamad, A., Sehnert, A. J., Rava,
R. P. (2012). Genome-wide fetal aneuploidy detection by maternal
plasma DNA sequencing. Obstet. Gynecol. 119, 890-901. This value
was insufficient to classify this sample as having a full
chromosome aneuploidy, but it was consistent with the presence of a
large duplication. FIG. 68 shows the 1 Mb bin results for this
sample. All the chromosomes other than chromosome 6 showed z values
that clustered around 0. By focusing only on chromosome 6 (FIG.
68), the exact region of the 38 Mb duplication was identified. This
38 Mb corresponded to the large duplication seen in the microarray
karyotype, and the ff calculated from this duplication was 11.9%.
The second duplication in the microarray karyotype was not detected
a priori by our criteria; however, it can be clearly seen in the
100 kb bin expansion of the region (FIG. 68). Improved analytic
methodology and/or deeper sequencing would clearly allow this
duplication to be detected. Finally, a 300 kb gain in chromosome 7
at 7q22.1 was also identified by MPS in agreement with the
microarray results (Table 31).
TABLE-US-00032 TABLE 31 MPS results on clinical samples that are
congruent with the clinically reported karyotype. Patient Affected
Gain/ Start End Size Chromosome ID Chr Loss bin bin (Mbp) region
C65104 6 Gain 64 102 38 6q12- 6q16.3 7 Gain 98.1 98.3 0.3 7q22.1
C61154 7 Loss 150.3 150.6 0.3 7q36.1 C61731 8 Loss 2 12 10 8p23.2-
8p23.2 C62228 15 Loss 23 39 16 15q11.2- 15q14 C60193 17 Gain 62 81
19 17q23.3- 17q25.3 10 Loss 134 135 2 10q26.3 C61233 3 Gain 158 198
40 3q25.32- 3q29 X Loss 1 10 9 Xp22.33- Xp22.31
Sample C61154 came from a pregnant woman carrying a fetus with
a7q36.1 deletion detected by metaphase karyotype analysis of
chorionic villi. FIG. 69 shows the 1 Mb bin results for this
sample. Only chromosomes 7 and 8 showed 1 Mb bins with z values
that met our criteria for classification. Chromosome 7 showed a
single 1 Mb bin with a significant decrease in the z value at
7q36.1 (denoted by circle in FIG. 69). An examination of the data
at higher resolution (100 kb bins) (FIG. 69) showed a deletion of
approximately 300 kb, which was consistent with the karyotype
report (Table 31). In this sample it was also observed an
approximately 1 Mb deletion in both the 1 Mb and 100 kb bin data
close to the centromere of chromosome 8 (as shown by the oval in
FIG. 69). The chromosome 8 deletion was not reported in the
karyotype obtained from chorionic villi (Table 32). The ffs
calculated from the chromosome 7 and 8 deletions were 18.4% and
68.5%, respectively. The ff calculated from chromosome X was 2.8%.
In this case, the high ff value for chromosome 8 indicated that
this deletion, which was not reported in the fetal metaphase
karyotype, was maternal in origin. In addition, the discordant
value of the chromosome 7 compared to chromosome X ff values
suggests that part of the signal could be due to the mother. The
karyotype report indicated that the chromosome 7 "abnormality is
most likely a derivative from a carrier parent," which is
consistent with the MPS data.
Sample C61731 had a partial deletion of the short arm of chromosome
8. The 1 Mb bin results (FIG. 70) indicated an approximately 5 Mb
deletion in the p-arm of chromosome 8 in agreement with the
karyotype (Table 31). The fetal fraction calculated from this
chromosome deletion was 8.4%.
Translocations
The fetal karyotype for sample C62228 showed an unbalanced
translocation consisting of 45, XX, -15, der(21) t (15;21)
(q15;p11.2). The 1 Mb bin results for this sample are shown in FIG.
71. There was a clear 20 Mb deletion in chromosome 15 in agreement
with the karyotype (Table 31). The ff calculated from the
chromosome 15 deletion was 11.3%. No sub-chromosomal abnormalities
were detected in the chromosome 21 data to indicate the
translocation breakpoint.
Identification of Additional Material Not Identified by
Karyotype
Two maternal samples had fetal karyotypes with added material of
unknown origin at specific chromosomes. The 1 Mb bin results for
sample C60193 are shown in FIG. 72. From the MPS data, the
additional material of unknown origin on the long arm of chromosome
10 appeared to be derived from an approximately 17 Mb duplication
at the q terminus of chromosome 17. There was also an approximately
2 Mb deletion at the q terminus of chromosome 10 that was confirmed
by the 100 kb bin data. The ffs calculated from the chromosome 17
duplication and chromosome X (male fetus) were 12.5% and 9.4%,
respectively. The 2 Mb deletion on chromosome 10 had a calculated
ff of 19.4%. Finally, the MPS results for this sample indicated a
small (300 kb) deletion in chromosome 7 that was not reported in
the metaphase karyotype (Table 32).
The 1 Mb bin results for sample C61233 are shown in FIG. 73. The
karyotype for this sample indicated additional chromosomal material
on the short arm of one of the X chromosomes. The additional
material of unknown origin appeared to originate from a 40 Mb
duplication at the q terminus of chromosome 3. There was also an
approximately 9 Mb deletion on the p arm of chromosome X (Table
31). The ffs calculated from the chromosome 3 duplication and
chromosome X deletion were 9.5% and 6.7%, respectively. The MPS
results for this sample also indicated three small sub-chromosomal
changes that were not reported in the metaphase karyotype (Table
32).
TABLE-US-00033 TABLE 32 Copy number variants detected by MPS that
were not reported in the clinical karyotypes. Pat Affected Gain/
Start End Size Chromosome ID Chr Loss bin bin (Mbp) region C60715 2
Gain 87.3 87.9 0.6 2p11.2 2 Loss 89.8 90.2 0.5 2p11.2 C61154 8 Loss
46.9 47.7 0.9 8q11.1 C60193 7 Loss 158.7 158.9 0.3 7q36.3 C61233 3
Loss 114 114.5 0.6 3q13.31 11 Loss 55.3 85.4 0.2 11q11 17 Gain 81
81.1 0.2 17q25.3 C61183 1 Loss 12.8 13 0.3 1p36.21 C65664 7 Loss
39.3 40 0.8 7p14.1 14 Loss 58 58.1 0.2 14q23.1 C66515 9 Gain 40.7
41 0.4 9p31.1 C60552 6 Loss 151.4 151.5 0.2 6q25.1 22 Gain 25.6
25.9 0.4 22q11.23
Mosaic Karyotypes
Four of the samples listed in Table 30 (C61183, C65664, C66515,
C60552) had mosaic karyotypes with sub-chromosomal abnormalities.
Unfortunately for three of the samples (C61183, C66515, C60552) the
putative sub-chromosomal abnormality originates in regions of the
genome for which information is either unavailable in the genome
build or highly repetitive and not be accessible for analysis.
Thus, in this case, the process was unable to determine the
sub-chromosomal abnormalities reported in these three samples. The
z.sub.ij values were all close to and centered around zero. Sample
C65664 had a mosaic karyotype with isochromosome 20q, an
abnormality that is associated with an event secondary to post
zygotic error. Chen, C.-P. (2003) Detection of mosaic isochromosome
20q in amniotic fluid in a pregnancy with fetal arthrogryposis
multiplex congenita and normal karyotype in fetal blood and
postnatal samples of placenta, skin, and liver. Prenat. Diagn. 23,
85-87. Since cfDNA primarily originates from placental
cytotrophoblasts, it is not expected that this abnormality would be
detected using MPS. There were 1-2 small sub-chromosomal changes
detected in these samples by MPS that were not reported in the
karyotypes (Table 32).
Further Discussion
This example demonstrates that in non-mosaic cases, it is possible
to obtain a full fetal molecular karyotype using MPS of maternal
plasma cfDNA that is equivalent to CMA (chromosomal microarray),
and in some cases is better than a metaphase karyotype obtained
from chorionic villi or amniocytes. Such a non-invasive test could
have immediate clinical utility, particularly in rural areas where
invasive procedures are not readily available.
Using 25-mer tags at .about.10.sup.9 tags/sample, the results
indicate that sufficient precision can be obtained between
sequencing runs to reliably achieve 100 kb resolution across the
genome. Even greater resolution can be achieved with deeper
sequencing. The improvements in the v3 sequencing chemistry allowed
for the use of 25-mer tags, compared to the 36-mers used in
previous work. Bianchi, D. W., Platt, L. D., Goldberg, J. D.,
Abuhamad, A., Sehnert, A. J., Rava, R. P. (2012). Genome-wide fetal
aneuploidy detection by maternal plasma DNA sequencing. Obstet.
Gynecol. 119, 890-901. These short tags mapped with high efficiency
across the genome, and the quantitative behavior demonstrated with
the artificial mixture analyses validates the methodology. At
today's costs, this depth of sequencing is approximately $1,000 per
sample. This is comparable to the cost of a chromosome microarray
result, but employs a risk-free blood draw rather than an invasive
procedure. Deeper sequencing would allow for even finer resolution
at an additional cost. Thus, this type of analysis could be
implemented today as a reflex test when other clinical factors are
present (such as sonographically-detected anomalies that are not
typical of whole chromosome aneuploidy) when the patient declines
an invasive procedure or prefers a blood test.
The lack of results on the mosaic samples (except for the
artificial mixture) highlights the current limitations of both the
microarray and MPS approaches. Sub-chromosomal abnormalities that
originate in regions of the genome for which information is either
unavailable in the genome build or highly repetitive will not be
accessible for analysis. Such inaccessible genome regions are
typically focused in the telomeres and centromeres of different
chromosomes and in the short arms of acrocentric chromosomes. Also,
the lower fetal fraction for the mosaic portion will be more
challenging for detection and may require even deeper sequencing
for effective classification.
Metaphase cytogenetic analysis from cell cultures, while considered
"standard," has some limitations that need to be considered. For
example, the ability to detect sub-chromosomal abnormalities is
typically limited to sizes of 5 Mb or greater. This constraint is
what led to the recent recommendation of using CMAs as a first tier
test in clinical practice. Cell culture is biased towards the
detection of more stable chromosomal configurations over
significant structural alterations. In the case of fluorescence in
situ hybridization (FISH), only the regions of the genome that are
addressed by design of the FISH probes can be analyzed. Finally, as
shown here, in actual clinical practice metaphase karyotypes can be
reported to contain "chromosomal material of unknown origin." The
MPS methodology of measuring copy number variation introduced in
this work overcomes these limitations of karyotyping
Importantly, our results showed that MPS was able to identify the
potential source of the material of unknown origin for clinical
samples C60193 and C61233. In addition, the MPS data showed small
deletions in the termini of the chromosomes that the metaphase
karyotype indicated were the breakpoints for the unknown
chromosomal material in each of these samples. Such deletions at
the breakpoints of translocations have been reported repeatedly in
the literature. Howarth, K. D., Pole, J. C. M, Beavis, J. C.,
Batty, E. M., Newman, S., Bignell, G. R., and Edwards, P. A. W.
(2011) Large duplications at reciprocal translocation breakpoints
that might be the counterpart of large deletions and could arise
from stalled replication bubbles. Genome Res. 21, 525-534. Based on
these results, MPS may have the capabilities to identify both the
presence of a sub-chromosomal duplication and suggest a
translocation position based on small deletions (or duplications)
elsewhere in the genome.
The methodologies described in this example also have applications
beyond the determination of fetal sub-chromosomal abnormalities
from cfDNA in maternal plasma. Ultimately, MPS can be applied to
any mixed biological sample in which one wishes to determine the
sub-chromosomal abnormalities in the minor component, even when the
minor component represents only a few percent of the total DNA in
the specimen. In prenatal diagnostics, samples obtained from
chorionic villi could be analyzed for mosaic karyotypes or maternal
contamination. Outside of prenatal diagnosis, many different
cancers have been associated with copy number changes that could
potentially be detected from cfDNA in the blood of the patient or a
solid tumor sample that contains both normal and cancer cells. As
the cost of MPS continues to drop, it is expected that its
application for detecting sub-chromosomal abnormalities in mixed
samples will find broad clinical utility.
Determination of fetal sub-chromosome abnormalities using deep
sequencing of maternal plasma allows for a full molecular karyotype
of the fetus to be determined noninvasively.
* * * * *
References